In today’s world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (A...
In today’s world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence o...
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Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.
Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity lead...
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ISBN:
(纸本)9798400715938
Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity leads to subtle performance challenges. In particular, non-fatal errors - internal failures of remote procedure calls that do not cause top-level request failures - can accumulate along the critical path, inflating latency and wasting *** this work, we analyze over 11 billion RPCs across more than 6,000 microservices at Uber. Our study shows that nearly 29% of successful requests experience non-fatal errors that remain hidden in traditional monitoring. We propose a novel latency-reduction estimator (LR estimator) to quantify the potential benefit of eliminating these errors. Our contributions include a systematic study of RPC error patterns, a methodology to estimate latency reductions, and case studies demonstrating up to a 30% reduction in tail latency.
We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can gene...
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With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling res...
With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.
Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of bl...
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Internet of things (IoT) gadgets have transformed several industries, including engineering, medicine, and more, thanks to the meteoric rise in the number of connected smart devices. The core principle of using the In...
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ISBN:
(数字)9798350375442
ISBN:
(纸本)9798350375459
Internet of things (IoT) gadgets have transformed several industries, including engineering, medicine, and more, thanks to the meteoric rise in the number of connected smart devices. The core principle of using the Internet of Things is that it expedites the delivery of information while consuming very little energy. In a nutshell, the Internet of Things (IoT) is a system of interconnected computing devices, services, and data that allows everyday objects to communicate with one another and with their physical environments. Prior to being saved on the server, all data sent across the IoT network must first be aggregated in the Queuing Telemetry Transport protocol (QTTP) broker. Access control, authorization, data storage, secrecy, authentication, system construction, and organization are some of the primary security concerns surrounding the Internet of Things (IoT). Three innovative security frameworks—RSA, Advanced Encryption Standard, and Elliptical Curve Cryptography—are created to enhance the protection of data in powered IoT devices. The security of IoT data is ensured by integrating all cryptic algorithms with the Constrained Queue Telemetry Transport Protocol (CQTTP). Data transmission from each IoT node undergoes cryptographic processes before being aggregated in the CQTTP. Exchange of key approvals, confirmation, and preservation of application data contrast are all accomplished by this cryptic action. Encryption keys are crucial to the safety of the data ix. The primary security component that reveals how many distinct key values a key in a protocol may take is the key's size. As a result, this study uses a new random key size method to give keys of varying sizes to the data sent by each node. Cryptography is harder when key sizes are set to be variable and random. In order to prevent script XSS commands and incorrect characteristics, all communications are encrypted using a technology that often permits employing TLS instead of plain TCP. Consequently, th
We consider single-phase flow with solute transport where ions in the fluid can precipitate and form a mineral, and where the mineral can dissolve and release solute into the fluid. Such a setting includes an evolving...
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Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several location...
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ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.
programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen *** vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such cont...
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programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen *** vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such contracts will not be *** a vulnerable contract is instantiated on the blockchain and becomes the target of attacks,the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were *** this work,we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise(IoC)specially crafted for use in the *** definitions need to capture the side-effects of successful exploitation in the context of the Ethereum ***,we define a model for smart contract execution,comprising multiple abstraction levels that mirror the multiple views of code execution on a ***,we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue,a prototype tool for investigating Ethereum security *** results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less ***,they are contract-specific and suffer from false *** the other hand,fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation,which is nevertheless applicable for practical use.
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