Position-relative identities in the internet of things: An evolutionary GHT approach

The Internet of Things (IoT) will result in the deployment of many billions of wireless embedded systems creating interactive pervasive environments. It is envisaged that devices will cooperate to provide greater system knowledge than the sum of its parts. In an emergency situation, the flow of data across the IoT may be disrupted, giving rise to a requirement for machine-to-machine interaction within the remaining ubiquitous environment. Geographic hash tables (GHTs) provide an efficient mechanism to support fault-tolerant rendezvous communication between devices. However, current approaches either rely on devices being equipped with a GPS or being manually assigned an identity. This is unrealistic when the majority of these systems will be located inside buildings and will be too numerous to expect manual configuration. Additionally, when using GHT as a distributed data store, imbalance in the topology can lead to storage and routing overhead. This causes unfair work load, exhausting limited power supplies as well as causing poor data redundancy. To deal with these issues, we propose an approach that balances graph-based layout identity assignment, through the application of multifitness genetic algorithms. Our experiments show through simulation that our multifitness evolution technique improves on the initial graph-based layout, providing devices with improved balance and reachability metrics

An open learning system for special needs education

The field of special needs education in case of speech and language deficiencies has seen great success, utilizing a number of paper-based systems, to help young children experiencing difficulty in language acquisition and the understanding of languages. These systems employ card and paper-based illustrations, which are combined to create scenarios for children in order to expose them to new vocabulary in context. While this success has encouraged the use of such systems for a long time, problems have been identified that need addressing. This paper presents research toward the application of an Open Learning system for special needs education that aims to provide an evolution in language learning in the context of understanding spoken instruction. Users of this Open Learning system benefit from open content with novel presentation of keywords and associated context. The learning algorithm is derived from the field of applied computing in human biology using the concept of spaced repetition and providing a novel augmentation of the memorization process for special needs education in a global Open Education setting