5,275 research outputs found
Topology Control Algorithm considering Antenna Radiation Pattern in Three-Dimensional Wireless Sensor Networks
Topology control is a key issue of wireless sensor network to reduce energy consumption and communication collision. Topology control algorithms in three-dimensional space have been proposed by modifying existing two-dimensional algorithms. These algorithms are based on the theoretical assumption that transmission power is radiated equally to the all directions by using isotropic antenna model. However, isotropic antenna does not exist, which is hypothetical antenna to compare the real antenna performance. In the real network, dipole antenna is applied, and because of the radiation pattern, performance of topology control algorithm is degraded. We proposed local remapping algorithm to solve the problem and applied it to existing topology control algorithms. Simulation results show that our algorithm increases performance of existing algorithms and reduces power consumption
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
An Energy Balanced Dynamic Topology Control Algorithm for Improved Network Lifetime
In wireless sensor networks, a few sensor nodes end up being vulnerable to
potentially rapid depletion of the battery reserves due to either their central
location or just the traffic patterns generated by the application. Traditional
energy management strategies, such as those which use topology control
algorithms, reduce the energy consumed at each node to the minimum necessary.
In this paper, we use a different approach that balances the energy consumption
at each of the nodes, thus increasing the functional lifetime of the network.
We propose a new distributed dynamic topology control algorithm called Energy
Balanced Topology Control (EBTC) which considers the actual energy consumed for
each transmission and reception to achieve the goal of an increased functional
lifetime. We analyze the algorithm's computational and communication complexity
and show that it is equivalent or lower in complexity to other dynamic topology
control algorithms. Using an empirical model of energy consumption, we show
that the EBTC algorithm increases the lifetime of a wireless sensor network by
over 40% compared to the best of previously known algorithms
Topology Control for Maintaining Network Connectivity and Maximizing Network Capacity Under the Physical Model
In this paper we study the issue of topology control under the physical Signal-to-Interference-Noise-Ratio (SINR) model, with the objective of maximizing network capacity. We show that existing graph-model-based topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the on-going transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%-110% in terms of maximizing the network capacity
Paradigm and paradox in topology control of power grids
Corrective Transmission Switching can be used by the grid operator to relieve line overloading and voltage violations, improve system reliability, and reduce system losses. Power grid optimization by means of line switching is typically formulated as a mixed integer programming problem (MIP). Such problems are known to be computationally intractable, and accordingly, a number of heuristic approaches to grid topology reconfiguration have been proposed in the power systems literature. By means of some low order examples (3-bus systems), it is shown that within a reasonably large class of “greedy” heuristics, none can be found that perform better than the others across all grid topologies. Despite this cautionary tale, statistical evidence based on a large number of simulations using IEEE 118-bus systems indicates that among three heuristics, a globally greedy heuristic is the most computationally intensive, but has the best chance of reducing generation costs while enforcing N-1 connectivity. It is argued that, among all iterative methods, the locally optimal switches at each stage have a better chance in not only approximating a global optimal solution but also greatly limiting the number of lines that are switched.First author draf
Paradigm and Paradox in Topology Control of Power Grids
Corrective Transmission Switching can be used by the grid operator to relieve
line overloading and voltage violations, improve system reliability, and reduce
system losses. Power grid optimization by means of line switching is typically
formulated as a mixed integer programming problem (MIP). Such problems are
known to be computationally intractable, and accordingly, a number of heuristic
approaches to grid topology reconfiguration have been proposed in the power
systems literature. By means of some low order examples (3-bus systems), it is
shown that within a reasonably large class of greedy heuristics, none can be
found that perform better than the others across all grid topologies. Despite
this cautionary tale, statistical evidence based on a large number of
simulations using using IEEE 118- bus systems indicates that among three
heuristics, a globally greedy heuristic is the most computationally intensive,
but has the best chance of reducing generation costs while enforcing N-1
connectivity. It is argued that, among all iterative methods, the locally
optimal switches at each stage have a better chance in not only approximating a
global optimal solution but also greatly limiting the number of lines that are
switched
Topology Control in Heterogeneous Wireless Networks: Problems and Solutions
Previous work on topology control usually assumes homogeneous wireless nodes with uniform transmission ranges. In this paper, we propose two localized topology control algorithms for heterogeneous wireless multi-hop networks with nonuniform transmission ranges: Directed Relative Neighborhood Graph (DRNG) and Directed Local Spanning Subgraph (DLSS). In both algorithms, each node selects a set of neighbors based on the locally collected information. We prove that (1) the topologies derived under DRNG and DLSS preserve the network connectivity; (2) the out degree of any node in the resulting topology by DLSS is bounded, while the out degree cannot be bounded in DRNG; and (3) the topologies generated by DRNG and DLSS preserve the network bi-directionality
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