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检索条件"任意字段=First IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2005"

共 1160 条记录，以下是41-50 订阅

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Towards On-demand Gas Sensing 17

Towards On-demand Gas Sensing

引用

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

作者： Gherman, Markus-Philipp Cheng, Yun Gomez, Andres Saukh, Olga Graz Univ Technol Inst Tech Informat Graz Austria Swiss Fed Inst Technol Comp Engn & Networks Lab Zurich Switzerland Univ St Gallen Interact & Commun Based Syst St Gallen Switzerland Complex Sci Hub Vienna Vienna Austria

ISBN: (纸本)9781665439299

Low-power operation of power-hungry MOX sensors is usually achieved by duty cycling them periodically. However, a difficulty arises if a sensor is operated at irregular time intervals due to intermittent energy availability in energy-neutral or batteryless applications. In this work, we propose a compensation method which re-maps on-demand measurements to virtually duty-cycled readings in the value domain by taking the duration of the last off-time into account. We evaluate our compensation algorithm based on Sensirion's SGP30 sensor and achieve up to 79% accuracy improvement compared to uncompensated measurements.

关键词： sensor systems sensors distributed computing

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An IoT-Based System for Autonomous, Continuous, Real-Time Patient Monitoring and Its Application to Pressure Injury Management 17

An IoT-Based System for Autonomous, Continuous, Real-Time Pa...

引用

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

作者： Mansfield, Sam Vin, Eric Obraczka, Katia Univ Calif Santa Cruz Baskin Sch Engn Santa Cruz CA 95064 USA

ISBN: (纸本)9781665439299

In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is the first open system that integrates the basic patient monitoring workflow for continuous and autonomous operation and includes sensed data collection, storage, analysis, and real-time visualization. PIMAP's open design allows it to integrate a variety of sensors (custom and off-the-shelf), analytics, and visualization. Other novel features of PIMAP include its deployment flexibility, i.e., its ability to be deployed in different configurations depending on the specific application needs, setting, and resources, as well as PIMAP's self-profiling and self-tuning capabilities. While PIMAP can be applied to various patient monitoring applications and settings, in this paper we focus on the unsolved problem of preventing pressure injuries.

关键词： Patient monitoring Data visualization Open systems Data collection Real-time systems sensor systems sensors

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Batterfly: Battery-Free Daily Living Activity Recognition System through distributed Execution over Energy Harvesting Analog PIR sensors 17

Batterfly: Battery-Free Daily Living Activity Recognition Sy...

引用

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

作者： Stirapongsasuti, Sopicha Misaki, Shinya Matsui, Tomokazu Suwa, Hirohiko Yasumoto, Keiichi Nara Inst Sci & Technol Ikoma Nara 6300192 Japan

ISBN: (纸本)9781665439299

In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim to construct a system that can not only sense but also recognize activities of daily living (ADLs) using only power generated by EH sensors. To achieve this goal, in this paper, we propose a fully EH-based ADL recognition system called Batterfly, which consists of EH analog PIR sensor nodes that can operate with indoor light, continuously senses human movement, and recognizes daily activities through machine learning. We applied the distributed execution method of the activity recognition model with five sensor nodes to five types of activities by five participants, and found that the system could recognize them with an average accuracy of 63.59%, comparable to the performance of the centralized model running on a gateway.

关键词： wireless sensor network energy harvesting distributed computing

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Asynchronous Gossip in Smartphone Peer-to-Peer Networks 17

Asynchronous Gossip in Smartphone Peer-to-Peer Networks

引用

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

作者： Newport, Calvin Weaver, Alex Zheng, Chaodong Georgetown Univ Washington DC 20007 USA Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9781665439299

In this paper, we study gossip algorithms in communication models that describe the peer-to-peer networking functionality included in most standard smartphone operating systems. We begin by describing and analyzing a new synchronous gossip algorithm in this setting that features both a faster round complexity and simpler operation than the best-known existing solutions. We also prove a new lower bound on the rounds required to solve gossip that resolves a minor open question by establishing that existing synchronous solutions are within logarithmic factors of optimal. We then adapt our synchronous algorithm to produce a novel gossip strategy for an asynchronous model that directly captures the interface of a standard smartphone peer-to-peer networking library (enabling algorithms described in this model to be easily implemented on real phones). Using new analysis techniques, we prove that this asynchronous strategy efficiently solves gossip. This is the first known efficient asynchronous information dissemination result for the smartphone peer-to-peer setting. We argue that our new strategy can be used to implement effective information spreading subroutines in real world smartphone peer-to-peer network applications, and that the analytical tools we developed to analyze it can be leveraged to produce other broadly useful algorithmic strategies for this increasingly important setting.

关键词： gossip distributed algorithms peer-to-peer networks

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Cluster Enabled Routing Algorithm for Wireless sensor Networks: An Optimization and Future Prospective 2

Cluster Enabled Routing Algorithm for Wireless Sensor Networ...

引用

2nd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Korishetty, Sharanu Jawaligi, Shivkumar S. Sharnbasva University Dept.of e & Ce Kalaburagi India

ISBN: (纸本)9798350347456

Devices with sensors are rapidly becoming indispensable. As the technology behind sensors has improved, a wide variety of sensor-based automated systems have emerged. By relaying data from sensors to actuators, these systems can perform actions automatically. Researchers face difficulties in obtaining long-lasting efficacy from sensor nodes into wireless sensor networks (WSNs) because of inadequate resources, energy constraints, and interference. For WSNs to continue operating for as long as possible, it is essential that their limited supply of energy be conserved. technique to organize groups of sensor nodes into clusters of varying sizes, each of which will then send its data on to the central station. first, the CEOMS algorithm build energy density task of the network nodes, & using the energy density function, it designates the sensor nodes having maximum left-over energy like cluster heads. © 2023 ieee.

关键词： sensor nodes

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Network-Aware AutoML Framework for Software-Defined sensor Networks 17

Network-Aware AutoML Framework for Software-Defined Sensor N...

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

作者： Horsanali, Emre Yigit, Yagmur Secinti, Gokhan Karameseoglu, Aytac Canberk, Berk Istanbul Tech Univ Dept Comp Engn Istanbul Turkey BTS Grp Istanbul Turkey Istanbul Tech Univ Dept AI & Data Engn Istanbul Turkey

ISBN: (纸本)9781665439299

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the internet of things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a networka-ware automated machine learning (AutoML) framework, which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using the metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.

关键词： Automated Machine Learning (AutoML) Sofware-defined sensor Networks (SDSN) distributed Denial-of-Service (DDoS)

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Threat Modelling of IoT systems Using distributed Ledger Technologies and IOTA 17

Threat Modelling of IoT Systems Using Distributed Ledger Tec...

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

作者： Damianou, Amalia Khan, M. Amir Angelopoulos, Constantinos Marios Katos, Vasilios Bournemouth Univ Poole Dorset England

ISBN: (纸本)9781665439299

Internet of Things has emerged as a key technological enabler for broader socio-technical and socio-economic paradigms, such as smart cities and Circular Economy. However, IoT systems are characterised by constraints and limitations which in order to be overcome they need to be deployed in conjunction and in synergy with other emerging ICT. distributed Ledger Technologies (DLT) can help overcome challenges pertaining to data immutability, timeliness and security. However, the use of DLT does not satisfactorily mitigate security risks and vulnerabilities per se and currently cybersecurity aspects of IoT systems are addressed in a fragmented way. Furthermore, the conflict between the resource demanding Blockchains and the highly constrained nature of IoT devices hinders implementation efforts of corresponding systems. We consider networked systems that comprise both IoT and DLT technologies via the prism of Intelligent Transportation systems (ITS). We elicit a three-tier threat model identifying attack vectors at the Device, the Network and the DLT layers. The identified attacks are then ranked by using the DREAD ranking scheme. The use of the threat model is demonstrated on a novel proof-of-concept IoT networked system implemented using the IOTA Tangle distributed ledger, where it helps to critically appraise the design of the system against the most critical attacks. Furthermore, the developed system is among the first in the literature to demonstrate the synergy of IoT and DLT on actual constrained embedded devices. The performance evaluation provides insights showing that such systems can be efficient and suitable for real-life deployment.

关键词： distributed Ledger Technologies Internet of Things Intelligent Vehicles

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Anomaly detection of data and topology patterns in WSNs 17

Anomaly detection of data and topology patterns in WSNs

引用

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

作者： Zakrzewski, Robert Martin, Trevor Oikonomou, George Univ Bristol Commun Syst & Networks Bristol Avon England Univ Bristol Artificial Intelligence Grp Bristol Avon England

ISBN: (纸本)9781665439299

Wireless sensor networks are often distributed which makes detection of cyber-attacks or misconfiguration hard. Topology and data patterns change may result from attacks leading to the compromise of data and service availability or indicate operational problems. Graphs are often used to model topology and data paths to describe and compare state of a system. For anomaly detection, the definition of normal patterns, deviation from normal, and criteria when to declare anomaly are required. In this contribution the process of acquisition of normal patterns (ground truth), and criteria when to declare anomaly based on graph comparison are proposed. The anomaly detection is suitable for deployment at the edge of a network. Finally, the inability to define all security threats is addressed by a custom tree-based classifier which only requires normal patterns for training. A simulated wireless sensor network was used to acquire data and apply the method. Our experiments show that data and topology change can be detected at the edge of a network.

关键词： Anomaly detection Machine learning sensor network Data pattern Topology Graph Cyber-security Fault detection

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WiDeo: One Shot Domain Adaptation for Cross-Environment WiFi-Based Activity Classification Through RF-Visual Joint Embedding 17

WiDeo: One Shot Domain Adaptation for Cross-Environment WiFi...

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

作者： Islam, Md Tamzeed Nirjon, Shahriar Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781665439299

The effect of environmental change in WiFi signal is a major obstacle for generalized WiFi-based activity recognition. In this paper, we propose a novel method for adapting a WiFi-based activity classifier, that is trained on a large-scale dataset for an environment, to a new environment with only one labeled sample per activity. To this end, we propose a novel representation extraction algorithm using the supervision of visual data during the training phase. Our proposed feature extraction explicitly learns the corresponding relation between WiFi signal and movement of human body parts. For environment adaptation, we propose a framework that relies on only one labeled sample per activity class in comparison with current state-of-the-art solutions which are not suitable for few-shot adaptation. We collect data from four volunteers from five different environments and show that our proposed solution is able to achieve 28% higher accuracy than state-of-the-art solutions for environmental adaptation.

关键词： Training Visualization Adaptation models Correlation distributed databases Activity recognition Feature extraction

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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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