We present a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be...
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ISBN:
(纸本)9798350377712;9798350377705
We present a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be utilized to assess the impact of FDI attacks, benchmark attack detector performance, and validate the effectiveness of mitigation/reconfiguration strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework leverages high-fidelity simulations of Gazebo and a Motion Capture system to emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor measurements in real-time. We propose an empirical approach to faithfully recreate signal characteristics such as latency and noise in these measurements. Finally, we illustrate the efficacy of our proposed framework through a Mixed Reality experiment consisting of an emulated GNSS attack on an actual UAV, which (i) demonstrates the impact of false data injection attacks on GNSS measurements and (ii) validates a mitigation strategy utilizing a distributed camera network developed in our previous work. Our open-source implementation is available at https://***/CogniPilot/mixed_sense
Utility companies are expected to leverage the flexibility in residential demand and the penetration of smart/IoT devices to implement efficient and effective residential demand response schemes with automated device ...
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ISBN:
(纸本)9798350318562;9798350318555
Utility companies are expected to leverage the flexibility in residential demand and the penetration of smart/IoT devices to implement efficient and effective residential demand response schemes with automated device scheduling. The use of distributed architectures in these systems allows adversaries to employ false data injection attacks (FDIA) to manipulate optimal decisions, as utility companies do not have complete control over the systems and data. Thus, reliable implementations are required to ensure the resilience of residential DR schemes against FDIAs. In this work, we integrate DR optimisation, anomaly detection in the context of FDIA detection, and attack impact mitigation methods to implement a resilient, automated device scheduling framework. We evaluate the implemented framework using a real-world dataset to emphasise the robustness of the framework against FDIAs.
To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated...
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ISBN:
(纸本)9798350333398
To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for large-scale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.
The advent of connected autonomous vehicles (CAVs) is bringing forth a revolutionary new era of technology transforming transportation. For traffic to be optimized and safe, efficient vehicle-to-everything collaborati...
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ISBN:
(纸本)9798350361261;9798350361278
The advent of connected autonomous vehicles (CAVs) is bringing forth a revolutionary new era of technology transforming transportation. For traffic to be optimized and safe, efficient vehicle-to-everything collaboration and improved autonomous vehicles (AV) decision-making are crucial. It becomes essential to make decisions in real time using information from vehicle sensors, software, and traffic data. As a part of such an In-Vehicle Network (IVN), over-the-air (OTA) software update service in CAVs needs to be facilitated rapidly, reliably, and securely. However, by taking advantage of vulnerabilities, attackers may quickly target the OTA software update as part of botnets to execute distributed denial-of-service (DDoS) attacks. The enormous volume and widespread nature of these DDoS cyber-attacks make it vital for the CAV industry to work quickly on identifying and preventing these threats. This paper proposes proof-of-concept experiments with the Hyperledger Fabric (HLF) Blockchain model to detect and prevent DDoS attacks in CAVs' OTA update systems. The proposed method implements Practical Byzantine Fault Tolerance (PBFT) as the consensus mechanism and a distributed firewall to ensure the ledger is secure and tamper-proof. The system is tested on the Amazon Elastic Compute Cloud (EC2) Blockchain (BC) platform. The results show that the proposed approach effectively prevents DDoS attacks while ensuring fast transaction execution time.
Wireless Underwater sensor Networks serve a multitude of purposes, including disaster avoidance, contaminant monitoring, overseas research, oceanographic data acquisition, supported routing, and strategic surveillance...
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Medical Image AI systems can assist doctors in making diagnoses, thereby improving diagnostic accuracy. These systems are now widely used in hospitals. However, current AI diagnostic methods typically rely on various ...
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In the realm of edge intelligence, emerging video analytics applications are often based on resource constrained edge devices. These applications need systems which are able to provide both low-latency and high-accura...
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ISBN:
(纸本)9798350361360;9798350361353
In the realm of edge intelligence, emerging video analytics applications are often based on resource constrained edge devices. These applications need systems which are able to provide both low-latency and high-accuracy video stream processing, such as for object detection in real-time video streams. State-of-the-art systems tackle this challenge by leveraging edge computing and cloud computing. Such edge-cloud approaches typically combine low-latency results from the edge and high accuracy results from the cloud when processing a frame of the video stream. However, the accuracy achieved so far leaves much room for improvement. Furthermore, using more accurate object detection often requires having more capable hardware. This limits the edge devices which can be used. Applications related to autonomous drones, with the drone being the edge device, give one example. A wide variety of objects needs to be detected reliably for drones to operate safely. Drones with more computing capabilities are often more expensive and suffer from short battery life, as they consume more energy. In this paper, we introduce VATE, a novel edge-cloud system for object detection in real-time video streams. An enhanced approach for edgecloud fusion is presented, leading to improved object detection accuracy. A novel multi-object tracker is introduced, allowing VATE to run on less capable edge devices. The architecture of VATE enables it to be used when edge devices are capable of running on-device object detection frequently and when edge devices need to minimise on-device object detection to preserve battery life. Its performance is evaluated on a challenging, dronebased video dataset. The experimental results show that VATE improves accuracy by up to 27.5% compared to the state-of-theart system, while running on less capable and cheaper hardware.
State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states ...
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ISBN:
(纸本)9781665455336
State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states can be obtained through state estimation. This paper proposed a fast Gaussian-Newton state estimation method for power systems based on parallel belief propagation, which implements the Gaussian belief process via multi-core and multi-thread parallel computation to achieve efficient state estimation. Simulation findings on numerous ieee-standard power systems show that the suggested technique outperforms the traditional algorithm.
distributed clustering algorithms are employed in wireless sensor network (WSN) to improve the local data analysis. This process is carried out collaboratively with the help of nearby neighbours without a central cont...
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Deployment for underwater sensor networks (UWSNs) is one of the key issues for the topology management, which determines the overall network coverage and profoundly affects the network data collection performance. Thi...
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ISBN:
(纸本)9798350350227;9798350350210
Deployment for underwater sensor networks (UWSNs) is one of the key issues for the topology management, which determines the overall network coverage and profoundly affects the network data collection performance. This paper proposes a distributed energy-efficient self-adjusting UWSN deployment algorithm with the full consideration of the UWSN application scenarios, which includes two phases. In the initial deployment phase, the nodes at different positions will be assigned differential initial energy levels in different methods in accordance with the subsequent routing principles. During the redeployment phase, based on the virtual force theory, nodes will adjust their positions in a distributed way considering their neighbor nodes' positions, as well as the area and layer boundaries. Through the simulation experiments, the proposed algorithm can effectively improve the network coverage performance and significantly benefit the network routing process compared with the benchmark UWSN self-adjusting deployment algorithms.
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