7,845 research outputs found
Resource-aware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in
large-scale networks. When the 'things' coordinate decisions and act
collectively on shared information, feedback is introduced between them.
Multiple feedback loops are thus closed over a shared, general-purpose network.
Traditional feedback control is unsuitable for design of IoT control because it
relies on high-rate periodic communication and is ignorant of the shared
network resource. Therefore, recent event-based estimation methods are applied
herein for resource-aware IoT control allowing agents to decide online whether
communication with other agents is needed, or not. While this can reduce
network traffic significantly, a severe limitation of typical event-based
approaches is the need for instantaneous triggering decisions that leave no
time to reallocate freed resources (e.g., communication slots), which hence
remain unused. To address this problem, novel predictive and self triggering
protocols are proposed herein. From a unified Bayesian decision framework, two
schemes are developed: self triggers that predict, at the current triggering
instant, the next one; and predictive triggers that check at every time step,
whether communication will be needed at a given prediction horizon. The
suitability of these triggers for feedback control is demonstrated in hardware
experiments on a cart-pole, and scalability is discussed with a multi-vehicle
simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of
Things Journal. arXiv admin note: text overlap with arXiv:1609.0753
Event-triggered Learning
The efficient exchange of information is an essential aspect of intelligent
collective behavior. Event-triggered control and estimation achieve some
efficiency by replacing continuous data exchange between agents with
intermittent, or event-triggered communication. Typically, model-based
predictions are used at times of no data transmission, and updates are sent
only when the prediction error grows too large. The effectiveness in reducing
communication thus strongly depends on the quality of the prediction model. In
this article, we propose event-triggered learning as a novel concept to reduce
communication even further and to also adapt to changing dynamics. By
monitoring the actual communication rate and comparing it to the one that is
induced by the model, we detect a mismatch between model and reality and
trigger model learning when needed. Specifically, for linear Gaussian dynamics,
we derive different classes of learning triggers solely based on a statistical
analysis of inter-communication times and formally prove their effectiveness
with the aid of concentration inequalities
Event-based State Estimation: An Emulation-based Approach
An event-based state estimation approach for reducing communication in a
networked control system is proposed. Multiple distributed sensor agents
observe a dynamic process and sporadically transmit their measurements to
estimator agents over a shared bus network. Local event-triggering protocols
ensure that data is transmitted only when necessary to meet a desired
estimation accuracy. The event-based design is shown to emulate the performance
of a centralised state observer design up to guaranteed bounds, but with
reduced communication. The stability results for state estimation are extended
to the distributed control system that results when the local estimates are
used for feedback control. Results from numerical simulations and hardware
experiments illustrate the effectiveness of the proposed approach in reducing
network communication.Comment: 21 pages, 8 figures, this article is based on the technical report
arXiv:1511.05223 and is accepted for publication in IET Control Theory &
Application
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
Smart container monitoring using custom-made WSN technology : from business case to prototype
This paper reports on the development of a prototype solution for tracking and monitoring shipping containers. Deploying wireless sensor networks (WSNs) in an operational environment remains a challenging task. We strongly believe that standardized methodologies and tools could enhance future WSN deployments and enable rapid prototype development. Therefore, we choose to use a step-by-step approach where each step gives us more insight in the problem at hand while shielding some of the complexity of the final solution. We observed that environment emulation is of the utmost importance, especially for harsh wireless conditions inside a container stacking. This lead us to extend our test lab with wireless link emulation capabilities. It is also essential to assess feasibility of concepts and design choices after every stage during prototype development. This enabled us to create innovative WSN solutions, including a multi-MAC framework and a robust gateway selection algorithm
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
Autonomous Energy-aware production systems control
Energy and resource efficiency has recently become one of the most relevant topics of research in
manufacturing, both as industry accounts for a major part of the world energy consumption and in the
context of the increasing attention to the need of sustainable development at planetary level. This work aims
at paving the way to the development of novel energy-aware control policies of production systems, by
means of autonomous decisions about their states in terms of production and energy consumption, exploiting
the possibilities given by the new ICT technologies, such as Internet of Things and cloud computing, which
allow seamless information sharing among the machines through an appropriate and standardized ICT
infrastructure. The energy saving control approach investigated in this work exploits the current trend in
research to reduce the idle time of machines in favor of stand-by states, obtaining significant savings in terms
of energy, by allowing novel solutions for decentralized control. The proposed control enables the
production machines to autonomously share with and process the information of the other machines in the
system to decide in real-time their specific energy behaviour, even postponing processing if that is possible.
The approach adopted includes conceptual development of the dynamic behaviour models of the system and
the proposed policies, then their deployment in an application scenario taken by actual industry cases and
data, enabling study of the performance of the system with a detailed design of experiments. The proposed
approach represents a significant contribution to the state of the art, as the proposed energy-aware control
enables decisions based on real-time information instead of statistically-based forecasts of part arrival rates, as
in the previous literature; furthermore the approach is of relevant value for the practitioner, especially as it
paves the way to an operationalization to the vision of Cyber-Physical Systems and Industry 4.0
- …