11,044 research outputs found
Dynamic Vehicle Routing for Data Gathering in Wireless Networks
We consider a dynamic vehicle routing problem in wireless networks where
messages arriving randomly in time and space are collected by a mobile receiver
(vehicle or a collector). The collector is responsible for receiving these
messages via wireless communication by dynamically adjusting its position in
the network. Our goal is to utilize a combination of wireless transmission and
controlled mobility to improve the delay performance in such networks. We show
that the necessary and sufficient condition for the stability of such a system
(in the bounded average number of messages sense) is given by {\rho}<1 where
{\rho} is the average system load. We derive fundamental lower bounds for the
delay in the system and develop policies that are stable for all loads {\rho}<1
and that have asymptotically optimal delay scaling. Furthermore, we extend our
analysis to the case of multiple collectors in the network. We show that the
combination of mobility and wireless transmission results in a delay scaling of
{\Theta}(1/(1- {\rho})) with the system load {\rho} that is a factor of
{\Theta}(1/(1- {\rho})) smaller than the delay scaling in the corresponding
system where the collector visits each message location.Comment: 19 pages, 7 figure
Exponential penalty function control of loss networks
We introduce penalty-function-based admission control policies to
approximately maximize the expected reward rate in a loss network. These
control policies are easy to implement and perform well both in the transient
period as well as in steady state. A major advantage of the penalty approach is
that it avoids solving the associated dynamic program. However, a disadvantage
of this approach is that it requires the capacity requested by individual
requests to be sufficiently small compared to total available capacity. We
first solve a related deterministic linear program (LP) and then translate an
optimal solution of the LP into an admission control policy for the loss
network via an exponential penalty function. We show that the penalty policy is
a target-tracking policy--it performs well because the optimal solution of the
LP is a good target. We demonstrate that the penalty approach can be extended
to track arbitrarily defined target sets. Results from preliminary simulation
studies are included.Comment: Published at http://dx.doi.org/10.1214/105051604000000936 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Stochastic Online Shortest Path Routing: The Value of Feedback
This paper studies online shortest path routing over multi-hop networks. Link
costs or delays are time-varying and modeled by independent and identically
distributed random processes, whose parameters are initially unknown. The
parameters, and hence the optimal path, can only be estimated by routing
packets through the network and observing the realized delays. Our aim is to
find a routing policy that minimizes the regret (the cumulative difference of
expected delay) between the path chosen by the policy and the unknown optimal
path. We formulate the problem as a combinatorial bandit optimization problem
and consider several scenarios that differ in where routing decisions are made
and in the information available when making the decisions. For each scenario,
we derive a tight asymptotic lower bound on the regret that has to be satisfied
by any online routing policy. These bounds help us to understand the
performance improvements we can expect when (i) taking routing decisions at
each hop rather than at the source only, and (ii) observing per-link delays
rather than end-to-end path delays. In particular, we show that (i) is of no
use while (ii) can have a spectacular impact. Three algorithms, with a
trade-off between computational complexity and performance, are proposed. The
regret upper bounds of these algorithms improve over those of the existing
algorithms, and they significantly outperform state-of-the-art algorithms in
numerical experiments.Comment: 18 page
Distributed and Load-Adaptive Self Configuration in Sensor Networks
Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead
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