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1 |
Intelligent context-aware fog node discovery
کشف گره مه آگاه از زمینه هوشمند-2022 Fog computing has been proposed as a mechanism to address certain issues in
cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network
near the edge devices, which we called fog consumers. This decreases latency,
network bandwidth, and response time. Discovering the most relevant fog node,
the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed
Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner.
The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In
this study, the KNN, K-d tree, and brute force algorithms are used to discover
fog nodes based on the context-aware criteria of fog nodes and fog consumers.
The proposed framework is simulated using OMNET++, and the performance of
the proposed algorithms is compared based on performance metrics and execution
time. The accuracy and execution time are the major points of consideration in
the selection of an optimal fog search algorithm. The experiment results show
that the KNN and K-d tree algorithms achieve the same accuracy results of 95 %.
However, the K-d tree method takes less time to find the nearest fog nodes than
KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm
in the IDFD to discover the nearest fog nodes very efficiently and quickly.
keywords: Fog node | Discovery | Context-aware | Intelligent | Fog node discovery |
مقاله انگلیسی |
2 |
Efficient Implementation of Lightweight Hash Functions on GPU and Quantum Computers for IoT Applications
اجرای کارآمد توابع هش سبک در GPU و کامپیوترهای کوانتومی برای کاربردهای اینترنت اشیا-2022 Secure communication is important for Internet of Things (IoT) applications, to avoid cybersecurity attacks. One of the key security aspects is data integrity, which can be protected by employing cryptographic hash functions. Recently, US National Institute of Standards and Technology (NIST)
announced a competition to standardize lightweight hash functions, which can be used in IoT applications.
IoT communication involves various hardware platforms, from low-end microcontrollers to high-end cloud
servers with GPU accelerators. Since many sensor nodes are connected to the gateway devices and cloud
servers, performing high throughput integrity check is important to secure IoT applications. However, this is a
time consuming task even for high-end servers, which may affect the response time in IoT systems. Moreover,
no prior work had evaluated the performance of NIST candidates on contemporary processors like GPU and
quantum computers. In this study, we showed that with carefully crafted implementation techniques, all
the finalist hash function candidates in the NIST standardization competition can achieve high throughput
(up-to 1,000 Gbps) on a RTX 3080 GPU. This research output can be used by IoT gateway devices and cloud
servers to perform data integrity checks at high speed, thus ensuring a timely response. In addition, this is
also the first study that showcase the implementation of NIST lightweight hash functions on a quantum
computer (ProjectQ). Besides securing the communication in IoT, these efficient implementations on a GPU
and quantum computer can be used to evaluate the strength of respective hash functions against brute-force
attack.
INDEX TERMS: Graphics processing units (GPU) | hash function | lightweight cryptography | quantum computer. |
مقاله انگلیسی |
3 |
Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic
استفاده از اینترنت اشیا برای طراحی شبکه زنجیره تأمین امداد برای همه گیری SARS-COV-2-2021 The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient’s condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.© 2021 Elsevier B.V. All rights reserved. Keywords: Supply chain design | Epidemic outbreaks | Industry 4.0 | COVID-19 | SARS-COV-2 |
مقاله انگلیسی |
4 |
Attention as a source of variability in decision-making: Accounting for overall-value effects with diffusion models
توجه به عنوان منبع تغییرپذیری در تصمیم گیری: حسابداری اثرات ارزش کلی با مدل های انتشار-2021 Research has demonstrated that value-based decisions depend on not only the relative value difference
between options, but also their overall value. In particular, response times tend to decrease as the
overall (summed) value of the options increase. Standard sequential sampling models such as the
diffusion model can account for this fact by assuming that decision thresholds or noise vary with
overall value. Alternatively, gaze-based models that incorporate eye-tracking data can accommodate
this overall-value effect directly as a consequence of the multiplicative relationship between gaze and
option value. We compare the fit of non-gaze diffusion models to data simulated with a multiplicativegaze model. The results show how parameters related to decision thresholds or noise will vary as a
function of overall value, even when there is no such variability in the data generating process. In
empirical data, we find similar patterns where decision thresholds, noise, or non-decision time seem to
vary with overall value. Our results reveal that specific patterns in standard diffusion model parameters
can arise from a latent process of gaze-dependent evidence accumulation.
keywords: توجه | انتشار رانش | ارزش کلی | زمان پاسخ | ردیابی چشم | تصمیم گیری | Attention | Drift diffusion | Overall value | Response time | Eye tracking | Decision making |
مقاله انگلیسی |
5 |
Spare parts supply chain network modeling based on a novel scale-free network and replenishment path optimization with Q learning
مدل سازی شبکه زنجیره تامین قطعات یدکی بر اساس یک شبکه جدید بدون مقیاس و بهینه سازی مسیر پر کردن با یادگیری Q-2021 The efficiency of inventory management determines the customers’ buying experience, so a supply chain network with a shorter replenishment time is needed. The supply chain network is hoped to be robust to the stock-out of some distributors in the network under random customer demands. At the same time, replenishment path optimization method with the objective of minimizing the replenishment time is required. After reviewing previous work done in the field of supply network topology, scale-free network is proven to be efficient when it was used to model supply network. In addition, multi-agent based collaborative replenishment model is smarter. But, there is rare research on multi-agent based collaborative replenishment in the supply chain modelled by scale-free network. In this study, we proposed a spare parts supply chain network model based on a novel scale- free network. In this network growth process, the connection probability function of connecting new distributor to the existing distributors in the network, is constructed considering the connection number (for an existing distributor, its connection number means the number of other distributors which have collaborative relationship with it) and inventory capacity of the existing distributors and the transit time between new distributor and existing distributors. The connection probability function is built from the standpoints of both new distributor and the existing distributors. Furthermore, different selection policies are discussed in the network growth process to improve the efficiency. Unlike other replenishment path optimization methods, Q learning takes the advantage of interacting with the environment to make a dynamic decision. So, Q learning is selected to optimize the replenishment path in supply chain network. In the experiment, network static and dynamic performance is analyzed using the indicators: degree distribution, clustering coefficient, centrality and response time. Experi- mental results showed that the replenishment time of supply chain network which are optimized by Q learning is reduced by approximately 40%. So, the shorter replenishment time of the supply chain network is verified. Keywords: Spare parts supply chain network | Scale-free network | Q learning algorithm | Random customer demands |
مقاله انگلیسی |
6 |
An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic
An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic-2021 The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers’ skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand–supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19. Keywords: Risk and disruption | COVID-19 pandemic | Supply chain resilience | Essential item | Recovery strategy |
مقاله انگلیسی |
7 |
Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task
تحریک عصب واگ باعث کاهش یادگیری در یک کار یادگیری تقویتی بدون حرکت می شود-2020 When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well doc- umented. However, whether and how vagal feedback signals from the gut influence instru- mental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm in 39 healthy human participants (23 female) after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that taVNS acutely impaired decision-making, p = .041. Computational reinforcement learning models identified the cause of this as a re- duction in the learning rate through taVNS ( α= −0.092, p boot = .002), particularly after punishment ( αPun = −0.081, p boot = .012 vs. αRew = −0.031, p boot = .22). However, taVNS had no effect on go biases, Pavlovian response biases or response time. Hence, taVNS appeared to influence learning rather than action execution. These results highlight a novel role of vagal
afferent input in modulating reinforcement learning by tuning the learning rate according to
homeostatic needs. KEYWORDS : tVNS | Reinforcement learning | Computational modeling | Metabolic state | Instrumental acti |
مقاله انگلیسی |
8 |
Does voting on tax fund destination imply a direct democracy effect?
آیا رای دادن به مقصد صندوق مالیاتی بر اثر دموکراسی مستقیم دلالت دارد؟-2020 Does giving taxpayers a voice over the destination of tax revenues lead to more honest income declarations? Previous experiments have shown that giving participants the opportunity to select the
organization that receives their tax funds tends to increase tax compliance. The aim of this paper is
to assess whether this increase in compliance is induced by the sole fact of giving subjects a choice—a
“direct democracy effect”. To that aim, we ask participants to a tax evasion game to choose, in a collective
or individual choice setting, between two very similar organizations which provide the same social (ecological) benefits. We elicit compliance for both organizations before the choice is made so as to control for
the counter-factual compliance decision. We find that democracy does not increase compliance, and even
observe a slight negative effect—in particular for women. Our results confirm the existence of a commitment effect of democracy, leading to favor more the selected organization when it was actively chosen.
The commitment effect of democracy is however not enough to overcome the decrease in the level of compliance. Thanks to response times data, we show that prior choice on similar options as compared to a purely random selection weakens the preference for honesty. One important field application of our results is that democracy in tax spending must offer real choices to tax payers to improve compliance. Keywords: Commitment | Direct democracy effect | Voting | Tax evasion game |
مقاله انگلیسی |
9 |
Performance evaluation of web service response time probability distribution models for business process cycle time simulation
ارزیابی عملکرد مدلهای توزیع احتمال پاسخ زمان سرویس وب برای شبیه سازی چرخه فرآیند کسب و کار-2020 Context: The adoption of Business Process Management (BPM) is enabling companies to improve the pace
of building new capabilities, enhancing existing ones, and measuring process performance to identify bottlenecks. It is essential to compute the cycle time of the process to assess the performance of a business
process. The cycle time typically forms part of service level agreements (SLAs) and is a crucial contributor
to the overall user experience and productivity. The simulation technique is versatile and has broad applicability for determining realistic cycle time using historical data of web service response time. BPM tools
offer inadequate support for modeling input data used in simulation in the form of descriptive statistics or standard probability distributions like normal, lognormal, which results in inaccurate simulation
results.
Objective: We evaluate the effectiveness of different parametric and non-parametric probability distributions for modeling data of web service response time. We further assess how the choice of probability
distribution impacts the accuracy of the simulated cycle time of a business process. The work is the first
of such a study using real-world data for encouraging Business Process Simulation Specification (BPSim)
standard setters and BPM tools to enhance their support for such distributions in their simulation engine.
Method: We consider several parametric and non-parametric distributions and explore how well these
distributions fit web service response time from extensive public and a real-world dataset. The cycle time
of the business process of a real-world system is simulated using the identified distributions to model the
underlying web service data.
Results: Our results show that kernel distribution is the most suitable choice, followed by Burr. Kernel
outperforms Burr by 86.63% for the public and 84.21% for the real-world dataset. The choice of distribution affects the percentile ranks like 90 and above than the median. The use of single-point values
underestimates cycle time values at higher percentiles.
Conclusion: Based on our empirical results, we recommend the addition of kernel and Burr to the current
list of distributions supported by BPSim and BPM tools. Keywords: Simulation input modeling | Parametric distributions | Non-parametric distributions | Performance evaluation | Web service response time | Cycle time |
مقاله انگلیسی |
10 |
FaaVPP: Fog as a virtual power plant service for community energy management
FaaVPP: مه به عنوان یک سرویس نیروگاه مجازی برای مدیریت انرژی جامعه-2020 The fossil fuel based power generators emit CO2 and expensive electricity. In this paper, fog as a virtual
power plant (FaaVPP) is proposed to integrate power of distributed renewable power generators and
the utility for a community. A prosumer–consumer and service providing company oriented linear
model is proposed to minimize power consumption cost for prosumers and maximize profit for the
company. The mathematical proof of linear model validates the significance for service provider and
energy users. Moreover, outcome of case studies advocate the efficiency of the model. Efficient resource
utilization techniques of fog resources ensure the near-real time service provision to the community.
In the paper, effects of resource utilization techniques e.g., processing time (PT), response time (RT),
computing cost and energy consumed by the resources are also analyzed. Keywords: Virtual power plant | Fog as a service | FaaVPP | Computational energy | Response time | Processing time | Virtual retail energy provider |
مقاله انگلیسی |