24,757 research outputs found
QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks
We are given a set of sensors at given locations, a set of potential
locations for placing base stations (BSs, or sinks), and another set of
potential locations for placing wireless relay nodes. There is a cost for
placing a BS and a cost for placing a relay. The problem we consider is to
select a set of BS locations, a set of relay locations, and an association of
sensor nodes with the selected BS locations, so that number of hops in the path
from each sensor to its BS is bounded by hmax, and among all such feasible
networks, the cost of the selected network is the minimum. The hop count bound
suffices to ensure a certain probability of the data being delivered to the BS
within a given maximum delay under a light traffic model. We observe that the
problem is NP-Hard, and is hard to even approximate within a constant factor.
For this problem, we propose a polynomial time approximation algorithm
(SmartSelect) based on a relay placement algorithm proposed in our earlier
work, along with a modification of the greedy algorithm for weighted set cover.
We have analyzed the worst case approximation guarantee for this algorithm. We
have also proposed a polynomial time heuristic to improve upon the solution
provided by SmartSelect. Our numerical results demonstrate that the algorithms
provide good quality solutions using very little computation time in various
randomly generated network scenarios
Consistent Sensor, Relay, and Link Selection in Wireless Sensor Networks
In wireless sensor networks, where energy is scarce, it is inefficient to
have all nodes active because they consume a non-negligible amount of battery.
In this paper we consider the problem of jointly selecting sensors, relays and
links in a wireless sensor network where the active sensors need to communicate
their measurements to one or multiple access points. Information messages are
routed stochastically in order to capture the inherent reliability of the
broadcast links via multiple hops, where the nodes may be acting as sensors or
as relays. We aim at finding optimal sparse solutions where both, the
consistency between the selected subset of sensors, relays and links, and the
graph connectivity in the selected subnetwork are guaranteed. Furthermore,
active nodes should ensure a network performance in a parameter estimation
scenario. Two problems are studied: sensor and link selection; and sensor,
relay and link selection. To solve such problems, we present tractable
optimization formulations and propose two algorithms that satisfy the previous
network requirements. We also explore an extension scenario: only link
selection. Simulation results show the performance of the algorithms and
illustrate how they provide a sparse solution, which not only saves energy but
also guarantees the network requirements.Comment: 27 pages, 17 figure
Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network along a Line
We are motivated by the need, in some applications, for impromptu or
as-you-go deployment of wireless sensor networks. A person walks along a line,
starting from a sink node (e.g., a base-station), and proceeds towards a source
node (e.g., a sensor) which is at an a priori unknown location. At equally
spaced locations, he makes link quality measurements to the previous relay, and
deploys relays at some of these locations, with the aim to connect the source
to the sink by a multihop wireless path. In this paper, we consider two
approaches for impromptu deployment: (i) the deployment agent can only move
forward (which we call a pure as-you-go approach), and (ii) the deployment
agent can make measurements over several consecutive steps before selecting a
placement location among them (which we call an explore-forward approach). We
consider a light traffic regime, and formulate the problem as a Markov decision
process, where the trade-off is among the power used by the nodes, the outage
probabilities in the links, and the number of relays placed per unit distance.
We obtain the structures of the optimal policies for the pure as-you-go
approach as well as for the explore-forward approach. We also consider natural
heuristic algorithms, for comparison. Numerical examples show that the
explore-forward approach significantly outperforms the pure as-you-go approach.
Next, we propose two learning algorithms for the explore-forward approach,
based on Stochastic Approximation, which asymptotically converge to the set of
optimal policies, without using any knowledge of the radio propagation model.
We demonstrate numerically that the learning algorithms can converge (as
deployment progresses) to the set of optimal policies reasonably fast and,
hence, can be practical, model-free algorithms for deployment over large
regions.Comment: 29 pages. arXiv admin note: text overlap with arXiv:1308.068
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