940 research outputs found
A Survey on the Evolution of Stream Processing Systems
Stream processing has been an active research field for more than 20 years,
but it is now witnessing its prime time due to recent successful efforts by the
research community and numerous worldwide open-source communities. This survey
provides a comprehensive overview of fundamental aspects of stream processing
systems and their evolution in the functional areas of out-of-order data
management, state management, fault tolerance, high availability, load
management, elasticity, and reconfiguration. We review noteworthy past research
findings, outline the similarities and differences between early ('00-'10) and
modern ('11-'18) streaming systems, and discuss recent trends and open
problems.Comment: 34 pages, 15 figures, 5 table
Client-access protocols for replicated services
Published versio
Deep Reinforcement Learning for Distribution Network Operation and Electricity Market
The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges.
In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions
Secure Hop-by-Hop Aggregation of End-to-End Concealed Data in Wireless Sensor Networks
In-network data aggregation is an essential technique in mission critical
wireless sensor networks (WSNs) for achieving effective transmission and hence
better power conservation. Common security protocols for aggregated WSNs are
either hop-by-hop or end-to-end, each of which has its own encryption schemes
considering different security primitives. End-to-end encrypted data
aggregation protocols introduce maximum data secrecy with in-efficient data
aggregation and more vulnerability to active attacks, while hop-by-hop data
aggregation protocols introduce maximum data integrity with efficient data
aggregation and more vulnerability to passive attacks.
In this paper, we propose a secure aggregation protocol for aggregated WSNs
deployed in hostile environments in which dual attack modes are present. Our
proposed protocol is a blend of flexible data aggregation as in hop-by-hop
protocols and optimal data confidentiality as in end-to-end protocols. Our
protocol introduces an efficient O(1) heuristic for checking data integrity
along with cost-effective heuristic-based divide and conquer attestation
process which is in average -O(n) in the worst scenario- for
further verification of aggregated results
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi
Network slice reconfiguration by exploiting deep reinforcement learning with large action space
It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this paper, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose a discrete Branching Dueling Q-network (discrete BDQ) by incorporating the action branching architecture into DQN, for drastically decreasing the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms
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