4,143 research outputs found
Altruistic Autonomy: Beating Congestion on Shared Roads
Traffic congestion has large economic and social costs. The introduction of
autonomous vehicles can potentially reduce this congestion, both by increasing
network throughput and by enabling a social planner to incentivize users of
autonomous vehicles to take longer routes that can alleviate congestion on more
direct roads. We formalize the effects of altruistic autonomy on roads shared
between human drivers and autonomous vehicles. In this work, we develop a
formal model of road congestion on shared roads based on the fundamental
diagram of traffic. We consider a network of parallel roads and provide
algorithms that compute optimal equilibria that are robust to additional
unforeseen demand. We further plan for optimal routings when users have varying
degrees of altruism. We find that even with arbitrarily small altruism, total
latency can be unboundedly better than without altruism, and that the best
selfish equilibrium can be unboundedly better than the worst selfish
equilibrium. We validate our theoretical results through microscopic traffic
simulations and show average latency decrease of a factor of 4 from worst-case
selfish equilibrium to the optimal equilibrium when autonomous vehicles are
altruistic.Comment: Accepted to Workshop on the Algorithmic Foundations of Robotics
(WAFR) 201
Controlled Matching Game for Resource Allocation and User Association in WLANs
In multi-rate IEEE 802.11 WLANs, the traditional user association based on
the strongest received signal and the well known anomaly of the MAC protocol
can lead to overloaded Access Points (APs), and poor or heterogeneous
performance. Our goal is to propose an alternative game-theoretic approach for
association. We model the joint resource allocation and user association as a
matching game with complementarities and peer effects consisting of selfish
players solely interested in their individual throughputs. Using recent
game-theoretic results we first show that various resource sharing protocols
actually fall in the scope of the set of stability-inducing resource allocation
schemes. The game makes an extensive use of the Nash bargaining and some of its
related properties that allow to control the incentives of the players. We show
that the proposed mechanism can greatly improve the efficiency of 802.11 with
heterogeneous nodes and reduce the negative impact of peer effects such as its
MAC anomaly. The mechanism can be implemented as a virtual connectivity
management layer to achieve efficient APs-user associations without
modification of the MAC layer
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
Predictive use of the Maximum Entropy Production principle for Past and Present Climates
In this paper, we show how the MEP hypothesis may be used to build simple
climate models without representing explicitly the energy transport by the
atmosphere. The purpose is twofold. First, we assess the performance of the MEP
hypothesis by comparing a simple model with minimal input data to a complex,
state-of-the-art General Circulation Model. Next, we show how to improve the
realism of MEP climate models by including climate feedbacks, focusing on the
case of the water-vapour feedback. We also discuss the dependence of the
entropy production rate and predicted surface temperature on the resolution of
the model
Optimal Resource Allocation and Relay Selection in Bandwidth Exchange Based Cooperative Forwarding
In this paper, we investigate joint optimal relay selection and resource
allocation under bandwidth exchange (BE) enabled incentivized cooperative
forwarding in wireless networks. We consider an autonomous network where N
nodes transmit data in the uplink to an access point (AP) / base station (BS).
We consider the scenario where each node gets an initial amount (equal, optimal
based on direct path or arbitrary) of bandwidth, and uses this bandwidth as a
flexible incentive for two hop relaying. We focus on alpha-fair network utility
maximization (NUM) and outage reduction in this environment. Our contribution
is two-fold. First, we propose an incentivized forwarding based resource
allocation algorithm which maximizes the global utility while preserving the
initial utility of each cooperative node. Second, defining the link weight of
each relay pair as the utility gain due to cooperation (over noncooperation),
we show that the optimal relay selection in alpha-fair NUM reduces to the
maximum weighted matching (MWM) problem in a non-bipartite graph. Numerical
results show that the proposed algorithms provide 20- 25% gain in spectral
efficiency and 90-98% reduction in outage probability.Comment: 8 pages, 7 figure
Learning from past bids to participate strategically in day-ahead electricity markets
We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf
Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting
We consider the distributed channel selection problem in the context of
device-to-device (D2D) communication as an underlay to a cellular network.
Underlaid D2D users communicate directly by utilizing the cellular spectrum but
their decisions are not governed by any centralized controller. Selfish D2D
users that compete for access to the resources construct a distributed system,
where the transmission performance depends on channel availability and quality.
This information, however, is difficult to acquire. Moreover, the adverse
effects of D2D users on cellular transmissions should be minimized. In order to
overcome these limitations, we propose a network-assisted distributed channel
selection approach in which D2D users are only allowed to use vacant cellular
channels. This scenario is modeled as a multi-player multi-armed bandit game
with side information, for which a distributed algorithmic solution is
proposed. The solution is a combination of no-regret learning and calibrated
forecasting, and can be applied to a broad class of multi-player stochastic
learning problems, in addition to the formulated channel selection problem.
Analytically, it is established that this approach not only yields vanishing
regret (in comparison to the global optimal solution), but also guarantees that
the empirical joint frequencies of the game converge to the set of correlated
equilibria.Comment: 31 pages (one column), 9 figure
- …