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1 |
Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning
مدیریت منابع برای IoV به کمک هوش لبه (EI) با استفاده از یادگیری تقویتی الهام گرفته از پردازش کوانتومی-2022 Recent developments in the Internet of Vehicles
(IoV) enable interconnected vehicles to support ubiquitous
services. Various emerging service applications are promising to
increase the Quality of Experience (QoE) of users. On-board
computation tasks generated by these applications have heavily
overloaded the resource-constrained vehicles, forcing it to offload
on-board tasks to other edge intelligence (EI)-assisted servers.
However, excessive task offloading can lead to severe competition
for communication and computation resources among vehicles,
thereby increasing the processing latency, energy consumption,
and system cost. To address these problems, we investigate
the transmission-awareness and computing-sense uplink resource
management problem and formulate it as a time-varying Markov
decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL)
is proposed to develop an intelligence-oriented edge offloading
strategy. Specifically, the vehicle can flexibly choose the network
access mode and offloading strategy through two different radio
interfaces to offload tasks to multiaccess edge computing (MEC)
servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive
balance between these two aspects. Simulation results show that
the proposed algorithm can significantly reduce the transmission
latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL) |
مقاله انگلیسی |
2 |
Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments
به سمت امنیت و حفظ حریم خصوصی برای هوش مصنوعی لبه در محیط های بازاریابی دیجیتال مبتنی بر IoT / IoE-2020 Abstract—Edge Artificial Intelligence (Edge AI) is a crucial
aspect of the current and futuristic digital marketing Internet of
Things (IoT) / Internet of Everything (IoE) environment.
Consumers often provide data to marketers which is used to enhance
services and provide a personalized customer experience (CX).
However, use, storage and processing of data has been a key concern.
Edge computing can enhance security and privacy which has been
said to raise the current state of the art in these areas. For example,
when certain processing of data can be done local to where
requested, security and privacy can be enhanced. However, Edge AI
in such an environment can be prone to its own security and privacy
considerations, especially in the digital marketing context where
personal data is involved. An ongoing challenge is maintaining
security in such context and meeting various legal privacy
requirements as they themselves continue to evolve, and many of
which are not entirely clear from the technical perspective. This
paper navigates some key security and privacy issues for Edge AI in
IoT/IoE digital marketing environments along with some possible
mitigations. Keywords: edge security | edge privacy | edge AI | edge intelligence | artificial intelligence | AI | machine learning | ML | IoT | IoE | edge | cybersecurity | legal | law | digital marketing | smart | GDPR | CCPA | security | privacy |
مقاله انگلیسی |
3 |
Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy
هوش لبه مبتنی بر اجرای اقتصادی برای نیروگاه مجازی در اینترنت 5G انرژی-2020 Nowadays, with a large of complicated geography of Distributed Energy Sources (DES), how to integrate
distributed renewable energy source and reduce the operational costs by Virtual Power Plant (VPP) becomes
a mainstream problem in Internet of energy. The traditional method of energy integration and operational
cost optimization utilizes the cloud computing technology to centralized control the computational task, which
increases the burden of computing. According with the development of information communication technology,
such as Internet of Things and 5G, edge computing technology is an effective way to offload computational
task to the edge side of 5G networks. Moreover, with the increase of collected data, it becomes a key point to
effectively improve the computing power of edge nodes in edge computing. Currently, machine learning is an
effective way to process the big data. Based this situation, it leads the combination of machine learning and
edge computing. In this paper, the Edge Intelligence (EI) structure is proposed to solve the Economic Dispatch
Problem (EDP) in VPP of Internet of Energy. Compared with the traditional edge computing, the proposed EI
structure inherits its original features which reduce the burden of cloud computing, and also the proposed EI
structure improves the computational power of edge computing. Through the splitting model and deploying
the particle model in the terminal, it is facility to real-time control and take the less costs of VPP. Due to
the transmission between the splitting models with counterpart, it transmits the part information and gradient
information, which effectively reduces the consumption of communication. The proposed method has verified
the effectiveness and feasibility through the numerical experiments of real application data sets. Keywords: Virtual Power Plant | Machine leaning | Edge intelligence | Economic Dispatch |
مقاله انگلیسی |