35,136 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
Spatial CSMA: A Distributed Scheduling Algorithm for the SIR Model with Time-varying Channels
Recent work has shown that adaptive CSMA algorithms can achieve throughput
optimality. However, these adaptive CSMA algorithms assume a rather simplistic
model for the wireless medium. Specifically, the interference is typically
modelled by a conflict graph, and the channels are assumed to be static. In
this work, we propose a distributed and adaptive CSMA algorithm under a more
realistic signal-to-interference ratio (SIR) based interference model, with
time-varying channels. We prove that our algorithm is throughput optimal under
this generalized model. Further, we augment our proposed algorithm by using a
parallel update technique. Numerical results show that our algorithm
outperforms the conflict graph based algorithms, in terms of supportable
throughput and the rate of convergence to steady-state.Comment: This work has been presented at National Conference on Communication,
2015, held at IIT Bombay, Mumbai, Indi
Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
Sampling from the lattice Gaussian distribution plays an important role in
various research fields. In this paper, the Markov chain Monte Carlo
(MCMC)-based sampling technique is advanced in several fronts. Firstly, the
spectral gap for the independent Metropolis-Hastings-Klein (MHK) algorithm is
derived, which is then extended to Peikert's algorithm and rejection sampling;
we show that independent MHK exhibits faster convergence. Then, the performance
of bounded distance decoding using MCMC is analyzed, revealing a flexible
trade-off between the decoding radius and complexity. MCMC is further applied
to trapdoor sampling, again offering a trade-off between security and
complexity. Finally, the independent multiple-try Metropolis-Klein (MTMK)
algorithm is proposed to enhance the convergence rate. The proposed algorithms
allow parallel implementation, which is beneficial for practical applications.Comment: submitted to Transaction on Information Theor
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