3,168 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
Distributed Optimal Rate-Reliability-Lifetime Tradeoff in Wireless Sensor Networks
The transmission rate, delivery reliability and network lifetime are three
fundamental but conflicting design objectives in energy-constrained wireless
sensor networks. In this paper, we address the optimal
rate-reliability-lifetime tradeoff with link capacity constraint, reliability
constraint and energy constraint. By introducing the weight parameters, we
combine the objectives at rate, reliability, and lifetime into a single
objective to characterize the tradeoff among them. However, the optimization
formulation of the rate-reliability-reliability tradeoff is neither separable
nor convex. Through a series of transformations, a separable and convex problem
is derived, and an efficient distributed Subgradient Dual Decomposition
algorithm (SDD) is proposed. Numerical examples confirm its convergence. Also,
numerical examples investigate the impact of weight parameters on the rate
utility, reliability utility and network lifetime, which provide a guidance to
properly set the value of weight parameters for a desired performance of WSNs
according to the realistic application's requirements.Comment: 27 pages, 10 figure
Transform-based Distributed Data Gathering
A general class of unidirectional transforms is presented that can be
computed in a distributed manner along an arbitrary routing tree. Additionally,
we provide a set of conditions under which these transforms are invertible.
These transforms can be computed as data is routed towards the collection (or
sink) node in the tree and exploit data correlation between nodes in the tree.
Moreover, when used in wireless sensor networks, these transforms can also
leverage data received at nodes via broadcast wireless communications. Various
constructions of unidirectional transforms are also provided for use in data
gathering in wireless sensor networks. New wavelet transforms are also proposed
which provide significant improvements over existing unidirectional transforms
Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks
We explore the following fundamental question -
how fast can information be collected from a wireless sensor
network? We consider a number of design parameters such
as, power control, time and frequency scheduling, and routing.
There are essentially two factors that hinder efficient data
collection - interference and the half-duplex single-transceiver
radios. We show that while power control helps in reducing the
number of transmission slots to complete a convergecast under a
single frequency channel, scheduling transmissions on different
frequency channels is more efficient in mitigating the effects of
interference (empirically, 6 channels suffice for most 100-node
networks). With these observations, we define a receiver-based
channel assignment problem, and prove it to be NP-complete on
general graphs. We then introduce a greedy channel assignment
algorithm that efficiently eliminates interference, and compare
its performance with other existing schemes via simulations.
Once the interference is completely eliminated, we show that
with half-duplex single-transceiver radios the achievable schedule
length is lower-bounded by max(2nk − 1,N), where nk is the
maximum number of nodes on any subtree and N is the number
of nodes in the network. We modify an existing distributed time
slot assignment algorithm to achieve this bound when a suitable
balanced routing scheme is employed. Through extensive simulations,
we demonstrate that convergecast can be completed within
up to 50% less time slots, in 100-node networks, using multiple
channels as compared to that with single-channel communication.
Finally, we also demonstrate further improvements that are
possible when the sink is equipped with multiple transceivers
or when there are multiple sinks to collect data
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
- …