With the increase of Web of Things devices, fog computing has emerged as a promising solution to lower the communication overhead and congestion in the cloud. In fog computingsystems, microservices are deployed as co...
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ISBN:
(纸本)9783031503849;9783031503856
With the increase of Web of Things devices, fog computing has emerged as a promising solution to lower the communication overhead and congestion in the cloud. In fog computingsystems, microservices are deployed as containers, which usually require an orchestration tool like Kubernetes for service discovery, placement, and recovery. One key challenge in the orchestration of microservices is establishing service elasticity in case of unpredictable bursts of load. Commonly, a centralized autoscaler in the cloud dynamically adjusts the number of microservice instances depending on the metric values monitored from distributed fog nodes. However, monitoring an increasing number of microservice instances can cause excessive network overhead and delay the scaling reaction. We propose DESA, a DEcentralized Self-adaptive Autoscaler through which each microservice instance makes its own scaling decision adaptively, cloning or terminating itself through a self-monitoring process. We evaluate DESA in a simulated fog computing environment with different numbers of fog nodes. The results show that DESA successfully reduces the scaling reaction time in large-scale fog computingsystems compared to the centralized approach while resulting in a similar maximum number of instances and average CPU utilization during a burst of load.
In the past few years, much research has been done to develop Autonomous Vehicles (AVs) at a level where AVs can replace manual driving vehicles. Though AVs can potentially improve the quality of safe transportation b...
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We are currently living in the era of the Internet of Things (IoT) where numerous smart objects are surrounding us and reporting various information about our environment. The main challenge of these IoT devices is en...
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ISBN:
(纸本)9798350319439
We are currently living in the era of the Internet of Things (IoT) where numerous smart objects are surrounding us and reporting various information about our environment. The main challenge of these IoT devices is energy consumption since the lifetime of each device is tightly related to its limited battery capacity. For this reason, it is beneficial to keep the IoT nodes in sleeping mode and to wake them up only when they are required. Wake-up-based wireless sensors present nowadays a leading technology that shapes well this requirement. However, despite their important impact on extending the network lifetime, these sensors present a real obstacle for IoT routing protocols. Effectively, routing in IoT networks should be time efficient, whereas these sensors require additional time to wake up and to be prepared for data reception and forwarding. In this paper, we give a general overview of the wake-up-based sensors and detail the most important challenges facing these sensors in the scope of the routing protocols. We also survey and propose a taxonomy of the most important and recent routing protocols that have used these sensors.
Assembling large-scale genomes from massive number of high-throughput sequencing reads is a major problem in Computational Genomics. distributed sequence assemblers such as PaKman [1] are used extensively in national ...
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A distributed Shor's algorithm is used to factorize a semiprime with two quantum computers using quantum teleportation to transport quantum states of qubits in parallel. We propose another distributed Shor's a...
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The enormous interconnected devices or the 'Internet of Things' are one of the main concerns when it comes to dealing with the network latency and bandwidth. Regular cloud computing offerings are limited becau...
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The proceedings contain 12 papers. The topics discussed include: location and time based access security control scheme for mobile cloud computing;location and time based access security control scheme for mobile clou...
ISBN:
(纸本)9783903176607
The proceedings contain 12 papers. The topics discussed include: location and time based access security control scheme for mobile cloud computing;location and time based access security control scheme for mobile cloud computing;location and time based access security control scheme for mobile cloud computing;DPFTT: distributed particle filter for target tracking in the internet of things;comparative analysis of deep learning models for detecting jamming attacks in Wi-Fi network data;U-TOE: universal TinyML on-board evaluation toolkit for low-power IoT;location and time-based access security control scheme for mobile cloud computing;blockchain adapted to IoT via green mining and fractal proof of work;and Cubedate: securing software updates in orbit for low-power payloads hosted on CubeSats.
The study aims to explore the problem of the distributed cooperative optimization control for the leader-following interconnected nonlinear multi-agent systems(MASs) via a distributed dynamic integrated system optimiz...
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Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed...
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ISBN:
(纸本)9798350333398
Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed to protect FL against such attacks, attackers are continuously developing more advanced techniques to bypass these protection mechanisms. In this context, this paper proposes a novel attack mechanism that allows malicious users to optimize their crafted reports, maximizing potential damage while limiting the chances of being detected. Our proposed attack technique is a robust approach designed to bypass existing defense mechanisms in FL. Our contributions are mainly investigating the FL model attack from the attacker's perspective, proposing a model relaxation approach to optimize a single poisoning ratio variable, and formulating a compromise between the chances of being detected and the amount of damage that the attack could cause. Additionally, we introduce three new attack designs, namely DTA, ATA, and NEA, which maximize the effect of the attack. The proposed Distance Target Attack (DTA) minimizes the distance from the target attack model, while the Accuracy Target Attack (ATA) deteriorates the accuracy of the global model. Furthermore, the Number Estimation Attack (NEA) aims to maximize the expected number of attackers that could bypass the aggregation detection mechanisms. The numerical results based on the KDD dataset confirm the ability of the proposed approach to deteriorate the global model accuracy. The experiments showed that the proposed DTA, ATA, and NEA attacks can significantly reduce the accuracy of the global model. These results demonstrate also the effectiveness and robustness of the proposed attack mechanism in compromising the accuracy and security of FL models.
Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently...
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ISBN:
(数字)9781665497862
ISBN:
(纸本)9781665497862
Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the methods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Message Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.
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