4,650 research outputs found
On Leveraging Partial Paths in Partially-Connected Networks
Mobile wireless network research focuses on scenarios at the extremes of the
network connectivity continuum where the probability of all nodes being
connected is either close to unity, assuming connected paths between all nodes
(mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths
exist at all (delay-tolerant networks). In this paper, we argue that a sizable
fraction of networks lies between these extremes and is characterized by the
existence of partial paths, i.e. multi-hop path segments that allow forwarding
data closer to the destination even when no end-to-end path is available. A
fundamental issue in such networks is dealing with disruptions of end-to-end
paths. Under a stochastic model, we compare the performance of the established
end-to-end retransmission (ignoring partial paths), against a forwarding
mechanism that leverages partial paths to forward data closer to the
destination even during disruption periods. Perhaps surprisingly, the
alternative mechanism is not necessarily superior. However, under a stochastic
monotonicity condition between current v.s. future path length, which we
demonstrate to hold in typical network models, we manage to prove superiority
of the alternative mechanism in stochastic dominance terms. We believe that
this study could serve as a foundation to design more efficient data transfer
protocols for partially-connected networks, which could potentially help
reducing the gap between applications that can be supported over disconnected
networks and those requiring full connectivity.Comment: Extended version of paper appearing at IEEE INFOCOM 2009, April
20-25, Rio de Janeiro, Brazi
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Dynamic control of Coding in Delay Tolerant Networks
Delay tolerant Networks (DTNs) leverage the mobility of relay nodes to
compensate for lack of permanent connectivity and thus enable communication
between nodes that are out of range of each other. To decrease message delivery
delay, the information to be transmitted is replicated in the network. We study
replication mechanisms that include Reed-Solomon type codes as well as network
coding in order to improve the probability of successful delivery within a
given time limit. We propose an analytical approach that allows us to compute
the probability of successful delivery. We study the effect of coding on the
performance of the network while optimizing parameters that govern routing
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
Modelling MAC-Layer Communications in Wireless Systems
We present a timed process calculus for modelling wireless networks in which
individual stations broadcast and receive messages; moreover the broadcasts are
subject to collisions. Based on a reduction semantics for the calculus we
define a contextual equivalence to compare the external behaviour of such
wireless networks. Further, we construct an extensional LTS (labelled
transition system) which models the activities of stations that can be directly
observed by the external environment. Standard bisimulations in this LTS
provide a sound proof method for proving systems contextually equivalence. We
illustrate the usefulness of the proof methodology by a series of examples.
Finally we show that this proof method is also complete, for a large class of
systems
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