274 research outputs found
Online Pricing Incentive to Sample Fresh Information
Today mobile users such as drivers are invited by content providers (e.g.,
Tripadvisor) to sample fresh information of diverse paths to control the age of
information (AoI). However, selfish drivers prefer to travel through the
shortest path instead of the others with extra costs in time and gas. To
motivate drivers to route and sample diverse paths, this paper is the first to
propose online pricing for a provider to economically reward drivers for
diverse routing and control the actual AoI dynamics over time and spatial path
domains. This online pricing optimization problem should be solved without
knowing drivers' costs and even arrivals, and is intractable due to the curse
of dimensionality in both time and space. If there is only one non-shortest
path, we leverage the Markov decision process (MDP) techniques to analyze the
problem. Accordingly, we design a linear-time algorithm for returning optimal
online pricing, where a higher pricing reward is needed for a larger AoI. If
there are a number of non-shortest paths, we prove that pricing one path at a
time is optimal, yet it is not optimal to choose the path with the largest
current AoI. Then we propose a new backward-clustered computation method and
develop an approximation algorithm to alternate different paths to price over
time. Perhaps surprisingly, our analysis of approximation ratio suggests that
our algorithm's performance approaches closer to optimum given more paths.Comment: 14 pages, 13 figure
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure
In congestion games, users make myopic routing decisions to jam each other,
and the social planner with the full information designs mechanisms on
information or payment side to regulate. However, it is difficult to obtain
time-varying traffic conditions, and emerging crowdsourcing platforms (e.g.,
Waze and Google Maps) provide a convenient way for mobile users travelling on
the paths to learn and share the traffic conditions over time. When congestion
games meet mobile crowdsourcing, it is critical to incentive selfish users to
change their myopic routing policy and reach the best exploitation-exploration
trade-off. By considering a simple but fundamental parallel routing network
with one deterministic path and multiple stochastic paths for atomic users, we
prove that the myopic routing policy's price of anarchy (PoA) is larger than
, which can be arbitrarily large as discount factor
. To remedy such huge efficiency loss, we propose a selective
information disclosure (SID) mechanism: we only reveal the latest traffic
information to users when they intend to over-explore the stochastic paths,
while hiding such information when they want to under-explore. We prove that
our mechanism reduces PoA to be less than . Besides
the worst-case performance, we further examine our mechanism's average-case
performance by using extensive simulations.Comment: Online technical report for our forthcoming AAAI 2023 paper. 12
pages, 3 figure
- β¦