4,212 research outputs found
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
Maximum Multipath Routing Throughput in Multirate Wireless Mesh Networks
In this paper, we consider the problem of finding the maximum routing
throughput between any pair of nodes in an arbitrary multirate wireless mesh
network (WMN) using multiple paths. Multipath routing is an efficient technique
to maximize routing throughput in WMN, however maximizing multipath routing
throughput is a NP-complete problem due to the shared medium for
electromagnetic wave transmission in wireless channel, inducing collision-free
scheduling as part of the optimization problem. In this work, we first provide
problem formulation that incorporates collision-free schedule, and then based
on this formulation we design an algorithm with search pruning that jointly
optimizes paths and transmission schedule. Though suboptimal, compared to the
known optimal single path flow, we demonstrate that an efficient multipath
routing scheme can increase the routing throughput by up to 100% for simple
WMNs.Comment: This paper has been accepted for publication in IEEE 80th Vehicular
Technology Conference, VTC-Fall 201
Algorithms for Fast Aggregated Convergecast in Sensor Networks
Fast and periodic collection of aggregated data
is of considerable interest for mission-critical and continuous
monitoring applications in sensor networks. In the many-to-one
communication paradigm, referred to as convergecast, we focus
on applications wherein data packets are aggregated at each hop
en-route to the sink along a tree-based routing topology, and
address the problem of minimizing the convergecast schedule
length by utilizing multiple frequency channels. The primary
hindrance in minimizing the schedule length is the presence of
interfering links. We prove that it is NP-complete to determine
whether all the interfering links in an arbitrary network can
be removed using at most a constant number of frequencies.
We give a sufficient condition on the number of frequencies for
which all the interfering links can be removed, and propose a
polynomial time algorithm that minimizes the schedule length
in this case. We also prove that minimizing the schedule length
for a given number of frequencies on an arbitrary network is
NP-complete, and describe a greedy scheme that gives a constant
factor approximation on unit disk graphs. When the routing tree
is not given as an input to the problem, we prove that a constant
factor approximation is still achievable for degree-bounded trees.
Finally, we evaluate our algorithms through simulations and
compare their performance under different network parameters
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