We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite ...
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ISBN:
(纸本)9798350339826
We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite Trusted Execution Environments (TEEs) providing multiple security functionalities, timestamps from the Operating System are not covered. Nevertheless, some services require time for validating permissions or ordering events. To address that need, we introduce Triad, a trusted timestamp dispatcher of time readings. The solution provides trusted timestamps enforced by mutually supportive enclave-based clock servers that create a continuous trusted timeline. We leverage enclave properties such as forced exits and CPU-based counters to mitigate attacks on the server's timestamp counters. Triad produces trusted, confidential, monotonically-increasing timestamps with bounded error and desirable, non-trivial properties. Our implementation relies on Intel SGX and SCONE, allowing transparent usage. We evaluate Triad's error and behavior in multiple dimensions.
Developing Internet of Things (IoT) sensor networks across various use cases calls for effective failure detection and recovery systems. This research uses the Isolation Forest algorithm to provide a new method for au...
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The proceedings contain 85 papers. The topics discussed include: ambient intelligence ecosystem for elderly pattern detection and care using social robots;application for electronic signatures using blockchain technol...
ISBN:
(纸本)9798350351637
The proceedings contain 85 papers. The topics discussed include: ambient intelligence ecosystem for elderly pattern detection and care using social robots;application for electronic signatures using blockchain technology to support trust, sovereignty and privacy;assessing the syllogistic logic and fact-checking capabilities of large language models;PentraFormer: learning agents for automated penetration testing via sequence modeling;design and development of a real-time IoT infrastructure for a future workspace - energy efficiency and sustainability;enhancing visual inertial odometry performance using deep learning-based sensor fusion;development of a user-friendly and efficient control system for smart home;and a dynamic risk-aware routing recommendation using deep reinforcement learning.
Field monitoring with IoT sensors is now widely used in various industries because of the development of Industrial 4.0 and data-driven manufacturing, which brings some management challenges such as sensors in various...
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This article delves into heterogeneous computingsystems, which employ multi-core processors (CPUs) and graphics processing units (GPUs) concurrently, facilitating efficient handling of resource-intensive tasks demand...
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This paper proposes a design pattern for implementing Commercial Bank Digital Currency which consists of two main layers - distribution and user constructed on top of a permissioned blockchain network based on Hyperle...
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In distributed file systems (DFS), deadlocks present significant challenges that adversely affect both performance and reliability. Conventional methods for deadlock detection and resolution are typically resource-int...
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Against the backdrop of the rapid development of unmanned aerial vehicle (UAV) technology, onboard visual navigation systems have become the core support technology for achieving precise positioning and efficient map ...
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Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast...
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ISBN:
(纸本)9798350348439;9798350384611
Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate applications. In this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge's customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.
The proceedings contain 165 papers. The topics discussed include: overlapped mobile charging for sensor networks;SAFEPAY on ethereum: a framework for detecting unfair payments in smart contracts;continuous, real-time ...
ISBN:
(纸本)9781728170022
The proceedings contain 165 papers. The topics discussed include: overlapped mobile charging for sensor networks;SAFEPAY on ethereum: a framework for detecting unfair payments in smart contracts;continuous, real-time object detection on mobile devices without offloading;distributionally robust edge learning with dirichlet process prior;CAPMAN: cooling and active power management in *** battery supported devices;PerDNN: offloading deep neural network computations to pervasive edge servers;more realistic website fingerprinting using deep learning;exact consensus under global asymmetric byzantine links;communication-efficient decentralized learning with sparsification and adaptive peer selection;and energy efficient in-memory integer multiplication based on racetrack memory.
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