Thus, the joint coverage-connectivity optimization problem is formulated as a weighted multi-objective optimization problem of the following form: (3).
This paper presents a novel approach to the joint coverage-connectivity optimization problem by formulating it as a quadratic minimization problem.
Force-based mobility control methods that arise from our energy minimization framework are shown to be scalable and self-organized, they are able to track the network dynamics, retain physical accuracy, and provide joint coverage-connectivity optimization [7, 8].
Specifically, new methodologies need to provide: (1) accurate link physics models to precisely model and characterize the heterogeneous set of link states in the network, (2) distributed low complexity algorithms for dynamically optimizing network performance by providing the network with the required self-organizing capability, and (3) joint coverage-connectivity optimization.
In our previous work, we formulated a convex optimization method for joint coverage and connectivity control [7, 8].
Third, spatially close actions will have a joint effect via population-dynamical connectivity.
Future work should analyse these cases prospectively and in relation to other mutation groups, with joint longitudinal behavioural and neuroimaging (including connectivity-based) methods, and detailed genotypic stratification and histopathological correlation.
It is a cubic lattice, with a connectivity of three struts per joint, and is bending-dominated in its elasto-plastic response to all loading states except for hydrostatic: under a hydrostatic stress it exhibits stretching-dominated behaviour.
In particular, the project aimed to introduce local mentoring support, to work with cohorts of learners, and to ensure internet connectivity of all students, while building joint teaching capacity.
