1,257 research outputs found
Spiking Neural Networks -- Part III: Neuromorphic Communications
Synergies between wireless communications and artificial intelligence are
increasingly motivating research at the intersection of the two fields. On the
one hand, the presence of more and more wirelessly connected devices, each with
its own data, is driving efforts to export advances in machine learning (ML)
from high performance computing facilities, where information is stored and
processed in a single location, to distributed, privacy-minded, processing at
the end user. On the other hand, ML can address algorithm and model deficits in
the optimization of communication protocols. However, implementing ML models
for learning and inference on battery-powered devices that are connected via
bandwidth-constrained channels remains challenging. This paper explores two
ways in which Spiking Neural Networks (SNNs) can help address these open
problems. First, we discuss federated learning for the distributed training of
SNNs, and then describe the integration of neuromorphic sensing, SNNs, and
impulse radio technologies for low-power remote inference.Comment: Submitte
Intelligence at the Extreme Edge: A Survey on Reformable TinyML
The rapid miniaturization of Machine Learning (ML) for low powered processing
has opened gateways to provide cognition at the extreme edge (E.g., sensors and
actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research
field proposes to democratize the use of Machine Learning (ML) and Deep
Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly
energy-efficient pervasive devices capable of operating with less than a few
Milliwatts of power. Nevertheless, many solutions assume that TinyML can only
run inference. Despite this, growing interest in TinyML has led to work that
makes them reformable, i.e., work that permits TinyML to improve once deployed.
In line with this, roadblocks in MCU based solutions in general, such as
reduced physical access and long deployment periods of MCUs, deem reformable
TinyML to play a significant part in more effective solutions. In this work, we
present a survey on reformable TinyML solutions with the proposal of a novel
taxonomy for ease of separation. Here, we also discuss the suitability of each
hierarchical layer in the taxonomy for allowing reformability. In addition to
these, we explore the workflow of TinyML and analyze the identified deployment
schemes and the scarcely available benchmarking tools. Furthermore, we discuss
how reformable TinyML can impact a few selected industrial areas and discuss
the challenges and future directions
Resource Allocation through Auction-based Incentive Scheme for Federated Learning in Mobile Edge Computing
openMobile Edge Computing (MEC) combinedly with Federated Learning is con-
sidered as most capable solutions to AI-driven services. Most of the studies
focus on Federated Learning on security aspects and performance, but the re-
search is lacking to establish an incentive mechanism for the devices that are
connected with a server to perform different task. In MEC, edge nodes would
not participate voluntarily in learning process, nodes differ in the accusation of
multi-dimensional resources, which also affects the performance of federated
learning.
In a competitive market scenario, the auction game theory has been widely
popular for designing efficient resource allocation mechanisms, as it particularly
focuses on regulating the strategic interactions among the self-interested play-
ers.In this thesis, I investigate auction-based approach that based on incentive
mechanism and encourage nodes to share their resources and take part in train-
ing process as well as to maximize the auction revenue. To achieve this research
goal, I developed auction mechanism considering the network dynamics and
neglecting the devices computation and design a novel generalized first price
auction mechanism to encourage participation of connected devices.
Furthermore, I studied the K top best-response bidding strategies that maximize
the profits of the resource sellers and guarantee the stability and effectiveness
of the auction by satisfying desired economic properties. To this end, I validate
the performance of the proposed auction mechanisms and bidding strategies
through numerical result analysis.Mobile Edge Computing (MEC) combinedly with Federated Learning is con-
sidered as most capable solutions to AI-driven services. Most of the studies
focus on Federated Learning on security aspects and performance, but the re-
search is lacking to establish an incentive mechanism for the devices that are
connected with a server to perform different task. In MEC, edge nodes would
not participate voluntarily in learning process, nodes differ in the accusation of
multi-dimensional resources, which also affects the performance of federated
learning.
In a competitive market scenario, the auction game theory has been widely
popular for designing efficient resource allocation mechanisms, as it particularly
focuses on regulating the strategic interactions among the self-interested play-
ers.In this thesis, I investigate auction-based approach that based on incentive
mechanism and encourage nodes to share their resources and take part in train-
ing process as well as to maximize the auction revenue. To achieve this research
goal, I developed auction mechanism considering the network dynamics and
neglecting the devices computation and design a novel generalized first price
auction mechanism to encourage participation of connected devices.
Furthermore, I studied the K top best-response bidding strategies that maximize
the profits of the resource sellers and guarantee the stability and effectiveness
of the auction by satisfying desired economic properties. To this end, I validate
the performance of the proposed auction mechanisms and bidding strategies
through numerical result analysis
Efficient and Secure Energy Trading with Electric Vehicles and Distributed Ledger Technology
Efficient energy management of Distributed Renewable Energy Resources (DRER)
enables a more sustainable and efficient energy ecosystem. Therefore, we
propose a holistic Energy Management System (EMS), utilising the computational
and energy storage capabilities of nearby Electric Vehicles (EVs), providing a
low-latency and efficient management platform for DRER. Through leveraging the
inherent, immutable features of Distributed Ledger Technology (DLT) and smart
contracts, we create a secure management environment, facilitating interactions
between multiple EVs and energy resources. Using a privacy-preserving load
forecasting method powered by Vehicular Fog Computing (VFC), we integrate the
computational resources of the EVs. Using DLT and our forecasting framework, we
accommodate efficient management algorithms in a secure and low-latency manner
enabling greater utilisation of the energy storage resources. Finally, we
assess our proposed EMS in terms of monetary and energy utility metrics,
establishing the increased benefits of multiple interacting EVs and load
forecasting. Through the proposed system, we have established the potential of
our framework to create a more sustainable and efficient energy ecosystem
whilst providing measurable benefits to participating agents.Comment: Accepted at IEEE Virtual Conference on Communications (VCC) 202
A Decade of Research in Fog computing: Relevance, Challenges, and Future Directions
Recent developments in the Internet of Things (IoT) and real-time
applications, have led to the unprecedented growth in the connected devices and
their generated data. Traditionally, this sensor data is transferred and
processed at the cloud, and the control signals are sent back to the relevant
actuators, as part of the IoT applications. This cloud-centric IoT model,
resulted in increased latencies and network load, and compromised privacy. To
address these problems, Fog Computing was coined by Cisco in 2012, a decade
ago, which utilizes proximal computational resources for processing the sensor
data. Ever since its proposal, fog computing has attracted significant
attention and the research fraternity focused at addressing different
challenges such as fog frameworks, simulators, resource management, placement
strategies, quality of service aspects, fog economics etc. However, after a
decade of research, we still do not see large-scale deployments of
public/private fog networks, which can be utilized in realizing interesting IoT
applications. In the literature, we only see pilot case studies and small-scale
testbeds, and utilization of simulators for demonstrating scale of the
specified models addressing the respective technical challenges. There are
several reasons for this, and most importantly, fog computing did not present a
clear business case for the companies and participating individuals yet. This
paper summarizes the technical, non-functional and economic challenges, which
have been posing hurdles in adopting fog computing, by consolidating them
across different clusters. The paper also summarizes the relevant academic and
industrial contributions in addressing these challenges and provides future
research directions in realizing real-time fog computing applications, also
considering the emerging trends such as federated learning and quantum
computing.Comment: Accepted for publication at Wiley Software: Practice and Experience
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