14,756 research outputs found
Coverage and Connectivity in Three-Dimensional Networks
Most wireless terrestrial networks are designed based on the assumption that
the nodes are deployed on a two-dimensional (2D) plane. However, this 2D
assumption is not valid in underwater, atmospheric, or space communications. In
fact, recent interest in underwater acoustic ad hoc and sensor networks hints
at the need to understand how to design networks in 3D. Unfortunately, the
design of 3D networks is surprisingly more difficult than the design of 2D
networks. For example, proofs of Kelvin's conjecture and Kepler's conjecture
required centuries of research to achieve breakthroughs, whereas their 2D
counterparts are trivial to solve. In this paper, we consider the coverage and
connectivity issues of 3D networks, where the goal is to find a node placement
strategy with 100% sensing coverage of a 3D space, while minimizing the number
of nodes required for surveillance. Our results indicate that the use of the
Voronoi tessellation of 3D space to create truncated octahedral cells results
in the best strategy. In this truncated octahedron placement strategy, the
transmission range must be at least 1.7889 times the sensing range in order to
maintain connectivity among nodes. If the transmission range is between 1.4142
and 1.7889 times the sensing range, then a hexagonal prism placement strategy
or a rhombic dodecahedron placement strategy should be used. Although the
required number of nodes in the hexagonal prism and the rhombic dodecahedron
placement strategies is the same, this number is 43.25% higher than the number
of nodes required by the truncated octahedron placement strategy. We verify by
simulation that our placement strategies indeed guarantee ubiquitous coverage.
We believe that our approach and our results presented in this paper could be
used for extending the processes of 2D network design to 3D networks.Comment: To appear in ACM Mobicom 200
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Jointly Optimizing Placement and Inference for Beacon-based Localization
The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
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