189 research outputs found
Intuitionistic fuzzy similarity measures and their role in classification
We present some similarity and distance measures between intuitionistic fuzzy sets (IFSs). Thus, we propose two semi-metric distance measures between IFSs. The measures are applied to classification of shapes and handwritten Arabic sentences described with intuitionistic fuzzy information. The experimental results permitted to do a comparative analysis between intuitionistic fuzzy similarity and distance measures, which can facilitate the selection of such measure in similar applications
On a reliable handoff procedure for supporting mobility in wireless sensor networks
Wireless sensor network (WSN) applications such as
patients’ health monitoring in hospitals, location-aware
ambient intelligence, industrial monitoring /maintenance
or homeland security require the support of mobile nodes
or node groups. In many of these applications, the lack of
network connectivity is not admissible or should at least be
time bounded, i.e. mobile nodes cannot be disconnected
from the rest of the WSN for an undefined period of time.
In this context, we aim at reliable and real-time mobility
support in WSNs, for which appropriate handoff and rerouting
decisions are mandatory. This paper1 drafts a
mechanism and correspondent heuristics for taking
reliable handoff decisions in WSNs. Fuzzy logic is used to
incorporate the inherent imprecision and uncertainty of
the physical quantities at stake
Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems
As deep neural networks continue to expand and become more complex, most edge
devices are unable to handle their extensive processing requirements.
Therefore, the concept of distributed inference is essential to distribute the
neural network among a cluster of nodes. However, distribution may lead to
additional energy consumption and dependency among devices that suffer from
unstable transmission rates. Unstable transmission rates harm real-time
performance of IoT devices causing low latency, high energy usage, and
potential failures. Hence, for dynamic systems, it is necessary to have a
resilient DNN with an adaptive architecture that can downsize as per the
available resources. This paper presents an empirical study that identifies the
connections in ResNet that can be dropped without significantly impacting the
model's performance to enable distribution in case of resource shortage. Based
on the results, a multi-objective optimization problem is formulated to
minimize latency and maximize accuracy as per available resources. Our
experiments demonstrate that an adaptive ResNet architecture can reduce shared
data, energy consumption, and latency throughout the distribution while
maintaining high accuracy.Comment: Accepted in the International Wireless Communications & Mobile
Computing Conference (IWCMC 2023
Tradeoffs between water uses and environmentalflows: a hydroeconomic analysis in the Ebro basin
Environmental water uses and their social values have been mostly overlooked in traditional water management over the last few decades, and recently, the maintenance of environmental flows has been considered a key issue in water policies. Addressing the more sustainable management of water resources involves introducing new water allocation policies. However, these policies are often associated with tradeoffs across sectors, stakeholders, and spatial locations. This study aims to evaluate the tradeoffs and political economy aspects of allocating water among economic water uses and environmental flows in water-scarce river basins. An empirical analysis has been conducted in the Ebro River basin (Spain) as a case study, where an intense debate on the environmental flow allocation of the Ebro mouth is taking place. The study uses a hydroeconomic model that includes the major water uses in the Ebro to analyze the effects of different water allocation policies under combinations of water availability and environmental flow scenarios. The results of this study highlight the importance of assessing the opportunity costs and political implications of reallocating water from economic activities to the environment under impending climate change impacts. Moreover, the results indicate that well-functioning water allocation policies should be not only economically efficient but also socially acceptable to reduce the likelihood of failure of water reallocation to the environment
RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos
© 2020 Elsevier B.V. With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.This work was supported by the Qatar Foundation
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
The vision of the upcoming 6G technologies, characterized by ultra-dense
network, low latency, and fast data rate is to support Pervasive AI (PAI) using
zero-touch solutions enabling self-X (e.g., self-configuration,
self-monitoring, and self-healing) services. However, the research on 6G is
still in its infancy, and only the first steps have been taken to conceptualize
its design, investigate its implementation, and plan for use cases. Toward this
end, academia and industry communities have gradually shifted from theoretical
studies of AI distribution to real-world deployment and standardization. Still,
designing an end-to-end framework that systematizes the AI distribution by
allowing easier access to the service using a third-party application assisted
by a zero-touch service provisioning has not been well explored. In this
context, we introduce a novel platform architecture to deploy a zero-touch
PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart
system. This platform aims to standardize the pervasive AI at all levels of the
architecture and unify the interfaces in order to facilitate the service
deployment across application and infrastructure domains, relieve the users
worries about cost, security, and resource allocation, and at the same time,
respect the 6G stringent performance requirements. As a proof of concept, we
present a Federated Learning-as-a-service use case where we evaluate the
ability of our proposed system to self-optimize and self-adapt to the dynamics
of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin
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