Spatially Embedded Networks
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The complexity of wireless communication networks has grown considerably in recent years. This has been driven in part by academic research that has started to define the information theoretic boundaries and advantages of certain complex networking topologies and protocols. On the other hand, the demands from consumers and industry have pushed wireless networks towards more sophisticated architectures and solutions, primarily in order to ensure a broad range of services can be delivered using a common infrastructure. This is particularly true of 4/5G technologies, which many believe should support all things for all people, including voice, data, public safety, distributed sensing and monitoring, etc. However, similar beliefs and trends can be found in other sectors, such as smart grid networks and even satellite networks.
It is important that engineers understand the global properties of complex networks, and how these properties arise from local structure. Such information can be fed into models and optimisation routines so that practical networks can be designed to perform as well as possible. A common approach to tackling complex problems is to exploit randomness and statistical properties of the underlying system. Probabilistic approaches to network modelling are not without their difficulties, and some of the main problems that researchers have struggled with over the years arise from the fact that networks are finite entities with physical boundaries.
Recent research by the investigators has focused on the effects that boundaries have on connectivity when networks are embedded in some finite spatial domain. Analytic expressions for the overall connection probability have been obtained. These formulae quantify the intuitive phenomenon that nodes near the boundary are more likely to disconnect, and thus they explain how the network outage probability behaves at high node densities. This work has been extended considerably to explore notions of resilience (k-connectivity), the effects of node directivity, diversity and power scaling laws, complicated geometric bounding domains (both convex and non-convex), and even the interplay between higher layer trust protocols and the physical network set-up and spatial domain.
In this project, the probabilistic formalism alluded to above will be exploited further to study several key concepts that influence the structure of spatially embedded networks. The following four topics will be treated:
- continuum models of spatially embedded networks, including the investigation of spectral and centrality properties of random networks;
- mobility models in spatially embedded networks, including random waypoint and Levy flight processes;
- trust models in spatially embedded networks, including trust dynamics and protocol design;
- temporal models of spatially embedded networks, including dynamical node and link (edge) models.
The work will take a mathematical approach, but will always maintain a focus on practical implications and designs.
University of Bristol | LEAD_ORG |
Toshiba Research Europe Ltd | COLLAB_ORG |
Carl Dettmann | PI_PER |
Subjects by relevance
- Information networks
- Networks (societal phenomena)
- Data communications networks
- Optimisation
- Social networks
- Wireless networks
Extracted key phrases
- Wireless communication network
- Spatially Embedded Networks
- Wireless network
- Complex network
- Physical network set
- Network outage probability
- Random network
- Smart grid network
- Practical network
- Satellite network
- Network modelling
- Trust model
- High layer trust protocol
- Information theoretic boundary
- Mobility model