285 research outputs found
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
Power assignment problems in wireless communication
A fundamental class of problems in wireless communication is concerned with the assignment of suitable transmission powers to wireless devices/stations such that the resulting communication graph satisfies certain desired properties and the overall energy consumed is minimized. Many concrete communication tasks in a wireless network like broadcast, multicast, point-to-point routing, creation of a communication backbone, etc. can be regarded as such a power assignment problem. This paper considers several problems of that kind; for example one problem studied before in (Vittorio Bil{\`o} et al: Geometric Clustering to Minimize the Sum of Cluster Sizes, ESA 2005) and (Helmut Alt et al.: Minimum-cost coverage of point sets by disks, SCG 2006) aims to select and assign powers to of the stations such that all other stations are within reach of at least one of the selected stations. We improve the running time for obtaining a -approximate solution for this problem from as reported by Bil{\`o} et al. (see Vittorio Bil{\`o} et al: Geometric Clustering to Minimize the Sum of Cluster Sizes, ESA 2005) to that is, we obtain a running time that is \emph{linear} in the network size. Further results include a constant approximation algorithm for the TSP problem under squared (non-metric!) edge costs, which can be employed to implement a novel data aggregation protocol, as well as efficient schemes to perform -hop multicasts
Distributed MST Computation in the Sleeping Model: Awake-Optimal Algorithms and Lower Bounds
We study the distributed minimum spanning tree (MST) problem, a fundamental
problem in distributed computing. It is well-known that distributed MST can be
solved in rounds in the standard CONGEST model (where
is the network size and is the network diameter) and this is
essentially the best possible round complexity (up to logarithmic factors).
However, in resource-constrained networks such as ad hoc wireless and sensor
networks, nodes spending so much time can lead to significant spending of
resources such as energy.
Motivated by the above consideration, we study distributed algorithms for MST
under the \emph{sleeping model} [Chatterjee et al., PODC 2020], a model for
design and analysis of resource-efficient distributed algorithms. In the
sleeping model, a node can be in one of two modes in any round --
\emph{sleeping} or \emph{awake} (unlike the traditional model where nodes are
always awake). Only the rounds in which a node is \emph{awake} are counted,
while \emph{sleeping} rounds are ignored. A node spends resources only in the
awake rounds and hence the main goal is to minimize the \emph{awake complexity}
of a distributed algorithm, the worst-case number of rounds any node is awake.
We present deterministic and randomized distributed MST algorithms that have
an \emph{optimal} awake complexity of time with a matching lower
bound. We also show that our randomized awake-optimal algorithm has essentially
the best possible round complexity by presenting a lower bound of
on the product of the awake and round complexity of any
distributed algorithm (including randomized) that outputs an MST, where
hides a factor.Comment: 28 pages, 1 table, 5 figures, abstract modified to fit arXiv
constraint
Minimum-energy broadcast in random-grid ad-hoc networks: approximation and distributed algorithms
The Min Energy broadcast problem consists in assigning transmission ranges to
the nodes of an ad-hoc network in order to guarantee a directed spanning tree
from a given source node and, at the same time, to minimize the energy
consumption (i.e. the energy cost) yielded by the range assignment. Min energy
broadcast is known to be NP-hard.
We consider random-grid networks where nodes are chosen independently at
random from the points of a square grid in the
plane. The probability of the existence of a node at a given point of the grid
does depend on that point, that is, the probability distribution can be
non-uniform.
By using information-theoretic arguments, we prove a lower bound
on the energy cost of any feasible solution for
this problem. Then, we provide an efficient solution of energy cost not larger
than .
Finally, we present a fully-distributed protocol that constructs a broadcast
range assignment of energy cost not larger than ,thus still yielding
constant approximation. The energy load is well balanced and, at the same time,
the work complexity (i.e. the energy due to all message transmissions of the
protocol) is asymptotically optimal. The completion time of the protocol is
only an factor slower than the optimum. The approximation quality
of our distributed solution is also experimentally evaluated.
All bounds hold with probability at least .Comment: 13 pages, 3 figures, 1 tabl
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