As edge computing continues to play a pivotal role in modern computing architectures, ensuring robust cybersecurity becomes imperative. This paper introduces our emerging results on a comprehensive approach to bolster...
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ISBN:
(纸本)9798350366266;9798350366259
As edge computing continues to play a pivotal role in modern computing architectures, ensuring robust cybersecurity becomes imperative. This paper introduces our emerging results on a comprehensive approach to bolster the cyber-resilience of edge computingsystems by incorporating the MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) loop. The proposed methodology involves the application of the MAPE-K loop at different levels of an edge computing architecture, aiming to create a holistic defense mechanism against cyber attacks. We present a prototype of our framework and assess its viability and efficacy by leveraging real-world edge devices deployed in an industrial production setting. Initial evidence from our results suggests that this novel approach leads us to reconsider how we construct more resilient edge computing architectures.
Communication needs in avionics and transportation have radically changed over the recent years. Traditionally, the underlying hard real-time networks were designed in a centralized way, focusing on redundancy and iso...
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ISBN:
(纸本)9798350371291;9798350371284
Communication needs in avionics and transportation have radically changed over the recent years. Traditionally, the underlying hard real-time networks were designed in a centralized way, focusing on redundancy and isolation. Today, real-time communication is ubiquitous, from large airplanes to small vehicles. The associated networks must support a wide range of applications, and large amounts of data. Centralized approaches from the avionics domain, e.g., AFDX, are too costly, too heavyweight, and not flexible enough for these applications. In this paper we explore a new distributed network architecture designed to support jumbo airliners, but also small aircraft and drones. Communication redundancy is achieved using redundant paths, which have to be adapted and optimized to the application. The main challenge then is to build an optimized network configuration ensuring safety, fault tolerance, timing, and performance of both critical, and non-critical communication. Minimizing volume and weight of the equipment is also mandatory. Since the solution space is too large to be explored in reasonable time, we propose a genetic algorithm. Our experiments show that our algorithm converges quickly and offers solutions of excellent quality. The computed solutions are in the top 2% among the best solutions obtained using an exhaustive exploration. Our approach thus enables system engineers to quickly explore and choose very good solution for their systems.
Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders...
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ISBN:
(纸本)9798350343946
Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for fog computing, as their focus on workload specification ignores the tight integration of application and infrastructure inherent in fog computing. In this paper, we outline an approach to a fog-native data processing benchmark that combines workload specifications with infrastructure specifications. This holistic approach allows researchers and engineers to quantify how a software approach performs for a given workload on given infrastructure. Further, by basing our benchmark in a realistic IoT sensor network scenario, we can combine paradigms such as low-latency event processing, machine learning inference, and offline data analytics, and analyze the performance impact of their interplay in a fog data processing system.
The terminal devices in the energy system face limitations in communication resources, storage space, computational power, and data security, making it challenging to train and deploy computationally intensive artific...
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ISBN:
(纸本)9798350361018;9798350361001
The terminal devices in the energy system face limitations in communication resources, storage space, computational power, and data security, making it challenging to train and deploy computationally intensive artificial intelligence models. Therefore, employing communication-efficient federated learning to train lightweight neural network models is a suitable solution. In this paper, we propose decentralized federated learning with efficient communication achieved by cyclically broadcasting weight parameters from each device during the model training process. We explore the proposed decentralized federated learning framework using energy disaggregation as a case study. Furthermore, grouped convolution is introduced to establish lightweight models, reducing the computational and storage costs during both the training process and model deployment. We conducted extensive experiments on publicly available datasets to validate that the proposed decentralized federated learning approach exhibits significant communication efficiency and minimal impact on the performance of the global model.
There are two parts to this study, and this is the second part. This paper assesses the performance of two distributed multi-reconfigurable intelligent surface (MRIS) assisted systems. Two MRIS-enhanced wireless commu...
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Metaverse, which enables the combination of the virtual and the physical worlds, requires mobile networks with high-capacity and reliable connectivity. Radio maps (RMs) can offer the knowledge of the wireless environm...
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ISBN:
(纸本)9798350361261;9798350361278
Metaverse, which enables the combination of the virtual and the physical worlds, requires mobile networks with high-capacity and reliable connectivity. Radio maps (RMs) can offer the knowledge of the wireless environments to improve the connectivity, by charactering the spatial distribution of received signal strength (RSS) throughout physical spaces. This paper investigates a collaborative RM reconstruction scheme, where client unmanned aerial vehicles (UAVs) collect RSS samples measured by mobile users for local training, while a server UAV performs model aggregation to optimize the global model. Unfortunately, RSS samples measured in practice can be sparse, non-uniformly distributed and non-independent and identically distributed (non-iid), such that reconstructing a complete RM is intractable. Therefore, we propose a novel RM reconstruction scheme based on federated learning (FL) with generative adversarial network (GAN), where GAN is exploited to generate a RM with sparsely and non-uniformly distributed RSS data. In order to tackle with non-iid RSS data, the FL is integrated with an adaptive client UAV selection strategy with model similarity evaluation, as well as a model weight assignment method with earth mover's distance evaluation for model aggregation. Simulation results reveal that benefiting from the aforementioned design, the proposed scheme can significantly enhance the reconstruction accuracy and convergence speed compared to the conventional algorithms.
The solution presented in this paper is an implementation based on cloud computing services for quality management systems. Thus, an architecture of the solution proposed by us will be presented, a presentation of the...
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distributed Denial-of-Service (DDoS) attacks pose formidable threats to the security and availability of critical Internet infrastructure. In-network computing technology brings new opportunities to address DDoS attac...
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ISBN:
(纸本)9781728190549
distributed Denial-of-Service (DDoS) attacks pose formidable threats to the security and availability of critical Internet infrastructure. In-network computing technology brings new opportunities to address DDoS attacks due to its intrinsic data plane programmability and high performance. However, existing DDoS attacks detection schemes based on in-network computing are difficult to strike a balance between true positive rate and false positive rate, especially in low-rate DDoS attacks scenarios. In response to this challenge, we propose RADD, an entropy-based method to detect DDoS attacks in real time based on in-network computing. RADD measures the distribution of network traffic from the perspective of individual IP address to discern subtle fluctuations within network traffic, hence providing early indications of potential DDoS attacks. We implement a prototype of RADD over programmable switches and results show that our proposed method significantly outperforms the state-of-the-art or has equivalent accuracy in low-rate and high-rate DDoS attacks scenarios.
Strong and flexible detection systems are vital to protect network infrastructures from distributed Denial of Service (DDoS) attacks, which are becoming more common and sophisticated. To detect DDoS attacks, this stud...
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Collaborative edge computing (CEC) is an emerging paradigm for heterogeneous devices to collaborate on edge computation jobs. For congestible links and computing units, delay-optimal forwarding and offloading for serv...
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ISBN:
(纸本)9781728190549
Collaborative edge computing (CEC) is an emerging paradigm for heterogeneous devices to collaborate on edge computation jobs. For congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with vertical split) in CEC remains an open problem. In this paper, we formulate the service chain forwarding and offloading problem in CEC with arbitrary topology and heterogeneous transmission/computation capability, and aim to minimize the aggregated network cost. We consider congestion-aware non-linear cost functions that cover various performance metrics and constraints, such as average queueing delay with limited processor capacity. We solve the non-convex optimization problem globally by analyzing the KKT condition and proposing a sufficient condition for optimality. We then propose a distributed algorithm that converges to the global optimum. The algorithm adapts to changes in input rates and network topology, and can be implemented as an online algorithm. Numerical evaluation shows that our method significantly outperforms baselines in multiple network instances, especially in congested scenarios.
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