Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

STCP: Receiver-agnostic Communication Enabled by Space-Time Cloud Pointers

Department of Electrical and Computer Engineering (Computer Engineering)During the last decade, mobile communication technologies have rapidly evolved and ubiquitous network connectivity is nearly achieved. However, we observe that there are critical situations where none of the existing mobile communication technologies is usable. Such situations are often found when messages need to be delivered to arbitrary persons or devices that are located in a specific space at a specific time. For instance at a disaster scene, current communication methods are incapable of delivering messages of a rescuer to the group of people at a specific area even when their cellular connections are alive because the rescuer cannot specify the receivers of the messages. We name this as receiver-unknown problem and propose a viable solution called SpaceMessaging. SpaceMessaging adopts the idea of Post-it by which we casually deliver our messages to a person who happens to visit a location at a random moment. To enable SpaceMessaging, we realize the concept of posting messages to a space by implementing cloud-pointers at a cloud server to which messages can be posted and from which messages can fetched by arbitrary mobile devices that are located at that space. Our Android-based prototype of SpaceMessaging, which particularly maps a cloud-pointer to a WiFi signal fingerprint captured from mobile devices, demonstrates that it first allows mobile devices to deliver messages to a specific space and to listen to the messages of a specific space in a highly accurate manner (with more than 90% of Recall)

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Evaluation of VRS-RTK GPS latency in a dynamic environment

This research project investigates the effects of latency in dynamic GPS (Global Positioning System) measurements made within a Virtual Reference Station (VRS) – Real Time Kinematic (RTK) network. The test method, which has been devised as an integral part of this research, allows for determination of the effects of latency in low speed dynamic VRS measurements. The method utilises a utility vehicle as a dynamic platform for testing, with a barcode reader attached to the vehicle to read barcodes which have been fixed to posts adjacent to the test path. Mounted in vertical alignment with the reader is the GPS antenna, providing the GPS signals to allow the GPS receiver(s) on board to determine the position of the vehicle as it passes the fixed barcodes and thereby providing a fixed frame of reference for the measurement of latency. Measuring lines in each direction and comparing the apparent position of the barcodes allows for the determination of latency. Conducting the testing procedure over a range of speeds will also facilitate the investigation of the relationship between platform speed and latency error. The results of this research have implications for any machine guidance and precision agriculture applications intending to use the VRS network where data accuracy is a major consideration. If the latency present in the positioning system has been quantified, it becomes possible to correct for this position error in real time. The research has only considered the effects of latency on the position solution, and this should be distinguished from accuracy. The accuracy of the VRS system in dynamic applications has not been investigated as part of this research project, but should also be considered in conjunction with latency when investigating the suitability of VRS to potential applications, dynamic or static

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems