6,855 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
Non-Cooperative Scheduling of Multiple Bag-of-Task Applications
Multiple applications that execute concurrently on heterogeneous platforms
compete for CPU and network resources. In this paper we analyze the behavior of
non-cooperative schedulers using the optimal strategy that maximize their
efficiency while fairness is ensured at a system level ignoring applications
characteristics. We limit our study to simple single-level master-worker
platforms and to the case where each scheduler is in charge of a single
application consisting of a large number of independent tasks. The tasks of a
given application all have the same computation and communication requirements,
but these requirements can vary from one application to another. In this
context, we assume that each scheduler aims at maximizing its throughput. We
give closed-form formula of the equilibrium reached by such a system and study
its performance. We characterize the situations where this Nash equilibrium is
optimal (in the Pareto sense) and show that even though no catastrophic
situation (Braess-like paradox) can occur, such an equilibrium can be
arbitrarily bad for any classical performance measure
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