153 research outputs found
Real-Time and Energy-Efficient Routing for Industrial Wireless Sensor-Actuator Networks
With the emergence of industrial standards such as WirelessHART, process industries are adopting Wireless Sensor-Actuator Networks (WSANs) that enable sensors and actuators to communicate through low-power wireless mesh networks. Industrial monitoring and control applications require real-time communication among sensors, controllers and actuators within end-to-end deadlines. Deadline misses may lead to production inefficiency, equipment destruction to irreparable financial and environmental impacts. Moreover, due to the large geographic area and harsh conditions of many industrial plants, it is labor-intensive or dan- gerous to change batteries of field devices. It is therefore important to achieve long network lifetime with battery-powered devices.
This dissertation tackles these challenges and make a series of contributions. (1) We present a new end-to-end delay analysis for feedback control loops whose transmissions are scheduled based on the Earliest Deadline First policy. (2) We propose a new real-time routing algorithm that increases the real-time capacity of WSANs by exploiting the insights of the delay analysis. (3) We develop an energy-efficient routing algorithm to improve the network lifetime while maintaining path diversity for reliable communication. (4) Finally, we design a distributed game-theoretic algorithm to allocate sensing applications with near-optimal quality of sensing
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Creating impact in real-world settings requires artificial intelligence
techniques to span the full pipeline from data, to predictive models, to
decisions. These components are typically approached separately: a machine
learning model is first trained via a measure of predictive accuracy, and then
its predictions are used as input into an optimization algorithm which produces
a decision. However, the loss function used to train the model may easily be
misaligned with the end goal, which is to make the best decisions possible.
Hand-tuning the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
We focus on combinatorial optimization problems and introduce a general
framework for decision-focused learning, where the machine learning model is
directly trained in conjunction with the optimization algorithm to produce
high-quality decisions. Technically, our contribution is a means of integrating
common classes of discrete optimization problems into deep learning or other
predictive models, which are typically trained via gradient descent. The main
idea is to use a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate this framework for
two broad classes of combinatorial problems: linear programs and submodular
maximization. Experimental results across a variety of domains show that
decision-focused learning often leads to improved optimization performance
compared to traditional methods. We find that standard measures of accuracy are
not a reliable proxy for a predictive model's utility in optimization, and our
method's ability to specify the true goal as the model's training objective
yields substantial dividends across a range of decision problems.Comment: Full version of paper accepted at AAAI 201
Artificial Intelligence Empowered UAVs Data Offloading in Mobile Edge Computing
The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs\u27 data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to a NE, and their trade-offs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios
Utility Design for Distributed Resource Allocation -- Part I: Characterizing and Optimizing the Exact Price of Anarchy
Game theory has emerged as a fruitful paradigm for the design of networked
multiagent systems. A fundamental component of this approach is the design of
agents' utility functions so that their self-interested maximization results in
a desirable collective behavior. In this work we focus on a well-studied class
of distributed resource allocation problems where each agent is requested to
select a subset of resources with the goal of optimizing a given system-level
objective. Our core contribution is the development of a novel framework to
tightly characterize the worst case performance of any resulting Nash
equilibrium (price of anarchy) as a function of the chosen agents' utility
functions. Leveraging this result, we identify how to design such utilities so
as to optimize the price of anarchy through a tractable linear program. This
provides us with a priori performance certificates applicable to any existing
learning algorithm capable of driving the system to an equilibrium. Part II of
this work specializes these results to submodular and supermodular objectives,
discusses the complexity of computing Nash equilibria, and provides multiple
illustrations of the theoretical findings.Comment: 15 pages, 5 figure
Study and Development of Power Control Schemes within a Cognitive Radio-based Game Theoretic Framework
Projecte final de carrera fet en col.laboració amb Nokia Siemens NetworksThe requirements of the International Telecommunication Union (ITU) for the 4th generation of
mobile devices raised up to 100 Mbps for high and 1Gbps for low mobility conditions. Reaching
such challenging targets requires the deployment of picocells and femtocells. These techniques
permit to achieve large cell capacity but also lead to di culties in terms of interference. The
GRACE algorithm, based on Cognitive Radio and Game Theory, has shown a fair balance
between cell capacity and outage as well as short convergence time, low complexity and easy
scalability. The aim of this work is to find an e cient power control algorithm that fits GRACE
these goals. Therefore, a study of Cognitive Radio, Game Theory and Power Control algorithms
is developed and a new power control algorithm is proposed. The simulation results show that
the Fractional Power Control can increase notably the outage performance and the energy saving
to the mobile devices
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