13,734 research outputs found
School Assignment, School Choice and Social Mobility
We estimate the chances of poor and non-poor children getting places in good schools, analysing the relationship between poverty, location and school assignment. Our dataset allows us to measure location and distance very precisely. The simple unconditional difference in probabilities of attending a good school is substantial. We run an analysis that controls completely for location, exploiting within-street variation and controlling for other personal characteristics. Children from poor families are significantly less likely to go to good schools. We show that the lower chance of poor children attending a good school is essentially unaffected by the degree of choice.School assignment, social mobility, school choice
Demo: Snap â Rapid Sensornet Deployment with a Sensornet Appstore
Despite ease of deployment being seen as a primary advantage
of sensor networks, deployment remains difficult.
We present Snap, a system for rapid sensornet deployment
that allows sensor networks to be deployed, positioned, and
reprogrammed through a sensornet appstore. Snap uses a
smartphone interface that uses QR codes for node identification, a map interface for node positioning, and dynamic loading of applications on the nodes. Snap nodes run the Contiki
operating system and its low-power IPv6 network stack that
provides direct access from nodes to the smartphone application.
We demonstrate rapid sensor node deployment, identification,
positioning, and node reprogramming within seconds, over
a multi-hop sensornet routing path with a WiFi-connected
smartphone
Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines
Autonomously detecting and recovering from faults is one approach for
reducing the operational complexity and costs associated with managing
computing environments. We present a novel methodology for autonomously
generating investigation leads that help identify systems faults, and extends
our previous work in this area by leveraging Restricted Boltzmann Machines
(RBMs) and contrastive divergence learning to analyse changes in historical
feature data. This allows us to heuristically identify the root cause of a
fault, and demonstrate an improvement to the state of the art by showing
feature data can be predicted heuristically beyond a single instance to include
entire sequences of information.Comment: Published and presented in the 11th IEEE International Conference and
Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014
A Low-Power CoAP for Contiki
Internet of Things devices will by and large
be battery-operated, but existing application protocols
have typically not been designed with power-efficiency in
mind. In low-power wireless systems, power-efficiency is
determined by the ability to maintain a low radio duty
cycle: keeping the radio off as much as possible. We
present an implementation of the IETF Constrained
Application Protocol (CoAP) for the Contiki operating system
that leverages the ContikiMAC low-power duty cycling
mechanism to provide power efficiency. We experimentally
evaluate our low-power CoAP, demonstrating that an
existing application layer protocol can be made power-efficient
through a generic radio duty cycling mechanism.
To the best of our knowledge, our CoAP implementation is
the first to provide power-efficient operation through radio
duty cycling. Our results question the need for specialized
low-power mechanisms at the application layer, instead
providing low-power operation only at the radio duty
cycling layer
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