Cloud infrastructures are evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds - often known as the Cloud-Edge Continuum. Continuum systems are dynamic, of...
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ISBN:
(纸本)9798331539580
Cloud infrastructures are evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds - often known as the Cloud-Edge Continuum. Continuum systems are dynamic, often massive in scale, and feature disparate infrastructure providers and platforms;this greatly increase the complexity of developing and managing applications. The Serverless paradigm shows the potential to greatly simplify the process of building Continuum applications - however, current scheduling mechanisms for Serverless Continuum platforms pay little attention to reducing the energy consumption and improving the sustainability of function execution. This is a significant omission, made worse as computing nodes within a Continuum may be powered by renewable energy sources that are intermittent and unpredictable, making low-powered and bottleneck nodes unavailable. There is great opportunity to design a decentralized energy management scheme for scheduling Serverless functions that takes advantage of the different layers of the Continuum, such as IoT devices located at the Edge, on-premises clusters closer to the data sources, or directly on large Cloud infrastructures. To achieve this, we formally model a green energy-aware Serverless workload scheduling problem for the multi-provider Cloud-Edge Continuum. We then design stable matching based technique for decentralized energy management (utilising a distributed controller) that considers the availability of green energy nodes and the QoS requirements of Serverless functions. We prove the complexity, stability and termination of the proposed heuristic algorithm, and also compare its performance with baseline scheduling techniques.
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.
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.
We consider two critical aspects of security in the distributedcomputing (DC) model: secure data shuffling and secure coded computing. It is imperative that any external entity overhearing the transmissions does not ...
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ISBN:
(纸本)9798350393194;9798350393187
We consider two critical aspects of security in the distributedcomputing (DC) model: secure data shuffling and secure coded computing. It is imperative that any external entity overhearing the transmissions does not gain any information about the intermediate values (IVs) exchanged during the shuffling phase of the DC model. Our approach ensures IV confidentiality during data shuffling. Moreover, each node in the system must be able to recover the IVs necessary for computing its output functions but must also remain oblivious to the IVs associated with output functions not assigned to it. We design secure DC methods and establish achievable limits on the tradeoffs between the communication and computation loads to contribute to the advancement of secure data processing in distributedsystems.
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.
Multi-agent, decentralised, and collective self-adaptive systems have been studied in a range of domains from smart-cities to the social dynamics of organisations. We present a novel application for research in this a...
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We propose a federated learning framework designed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to ...
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ISBN:
(纸本)9798350376975;9798350376968
We propose a federated learning framework designed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to focus on CUDA-based systems for enhancing the feasibility and cost-efficiency of machine learning in edge computing scenarios. Our framework is structured for straightforward deployment, addressing the complexities of initial federated learning project setups. We also built tensor manipulation library from scratch as the core of this framework. This contribution extends the reach of federated learning, enabling more flexible and inclusive approaches in distributed machine learning environments.
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