2,086 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
Power adjustment and scheduling in OFDMA femtocell networks
Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%
An Energy Efficient Self-healing Mechanism for Long Life Wireless Sensor Networks
In this paper, we provide an energy efficient self- healing mechanism for
Wireless Sensor Networks. The proposed solution is based on our probabilistic
sentinel scheme. To reduce energy consumption while maintaining good
connectivity between sentinel nodes, we compose our solution on two main
concepts, node adaptation and link adaptation. The first algorithm uses node
adaptation technique and permits to distributively schedule nodes activities
and select a minimum subset of active nodes (sentry) to monitor the interest
region. And secondly, we in- troduce a link control algorithm to ensure better
connectiv- ity between sentinel nodes while avoiding outliers appearance.
Without increasing control messages overhead, performances evaluations show
that our solution is scalable with a steady energy consumption. Simulations
carried out also show that the proposed mechanism ensures good connectivity
between sentry nodes while considerably reducing the total energy spent.Comment: 6 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1309.600
A short review on sleep scheduling mechanism in wireless sensor networks
Sleep scheduling, also known as duty cycling, which turn-
s sensor nodes on and off in the necessary time, is a common train of
thought to save energy. Sleep scheduling has become a significant mech-
anism to prolong the lifetime of WSNs and many related methods have
been proposed in recent years, which have diverse emphases and appli-
cation areas. This paper classifies those methods in different taxonomies
and provides a deep insight into them
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