48,788 research outputs found
Biologically inspired, self organizing communication networks.
PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in
Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical
resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive
multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and
Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets
including the targets’ previous locations is recorded as metadata to compute the target
sampling interval, target importance and local monitoring interval so that tracking
continuity and energy-efficiency are improved. The subsequent sensor groups that track
the targets are selected proactively according to the information associated with the
predicted target location probability such that the overall tracking performance is
optimized or nearly-optimized. One sensor node from each of the selected groups is
elected as a main node for management operations so that energy efficiency and load
balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes
that are located in the sensing areas of more than one target at the same time to decide
their preferred target according to the target importance and the distance to the target. A
tracking recovery mechanism is developed to provide the tracking reliability in the
event of target loss.
The problem of task mapping and scheduling in WSNs is also considered. A
Biological Independent Task Allocation (BITA) algorithm and a Biological Task
Mapping and Scheduling (BTMS) algorithm are developed to execute an application
using a group of sensor nodes. BITA, BTMS and the functional specialization of the
sensor groups in target tracking are all inspired from biological behaviours of
differentiation in zygote formation.
Simulation results show that compared with other well-known schemes, the
proposed tracking, task mapping and scheduling schemes can provide a significant
improvement in energy-efficiency and computational time, whilst maintaining
acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi
Degeneracy: a design principle for achieving robustness and evolvability
Robustness, the insensitivity of some of a biological system's
functionalities to a set of distinct conditions, is intimately linked to
fitness. Recent studies suggest that it may also play a vital role in enabling
the evolution of species. Increasing robustness, so is proposed, can lead to
the emergence of evolvability if evolution proceeds over a neutral network that
extends far throughout the fitness landscape. Here, we show that the design
principles used to achieve robustness dramatically influence whether robustness
leads to evolvability. In simulation experiments, we find that purely redundant
systems have remarkably low evolvability while degenerate, i.e. partially
redundant, systems tend to be orders of magnitude more evolvable. Surprisingly,
the magnitude of observed variation in evolvability can neither be explained by
differences in the size nor the topology of the neutral networks. This suggests
that degeneracy, a ubiquitous characteristic in biological systems, may be an
important enabler of natural evolution. More generally, our study provides
valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009
Refining interaction search through signed iterative Random Forests
Advances in supervised learning have enabled accurate prediction in
biological systems governed by complex interactions among biomolecules.
However, state-of-the-art predictive algorithms are typically black-boxes,
learning statistical interactions that are difficult to translate into testable
hypotheses. The iterative Random Forest algorithm took a step towards bridging
this gap by providing a computationally tractable procedure to identify the
stable, high-order feature interactions that drive the predictive accuracy of
Random Forests (RF). Here we refine the interactions identified by iRF to
explicitly map responses as a function of interacting features. Our method,
signed iRF, describes subsets of rules that frequently occur on RF decision
paths. We refer to these rule subsets as signed interactions. Signed
interactions share not only the same set of interacting features but also
exhibit similar thresholding behavior, and thus describe a consistent
functional relationship between interacting features and responses. We describe
stable and predictive importance metrics to rank signed interactions. For each
SPIM, we define null importance metrics that characterize its expected behavior
under known structure. We evaluate our proposed approach in biologically
inspired simulations and two case studies: predicting enhancer activity and
spatial gene expression patterns. In the case of enhancer activity, s-iRF
recovers one of the few experimentally validated high-order interactions and
suggests novel enhancer elements where this interaction may be active. In the
case of spatial gene expression patterns, s-iRF recovers all 11 reported links
in the gap gene network. By refining the process of interaction recovery, our
approach has the potential to guide mechanistic inquiry into systems whose
scale and complexity is beyond human comprehension
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